This article mainly demonstrates the efforts made by researchers in the relevant technologies of these three critical nodes from an engineering perspective, analyses the shortcomings of the current technology, and proposes a plan and prospects for the development of embryo engineering technology in the future.The normal development and maturation of oocytes and sperm, the formation of fertilized ova, the implantation of early embryos, and the growth and development of foetuses are the biological basis of mammalian reproduction. Therefore, research on oocytes has always occupied a very important position in the life sciences and reproductive medicine fields. Various embryo engineering technologies for oocytes, early embryo formation and subsequent developmental stages and different target sites, such as gene editing, intracytoplasmic sperm injection (ICSI), preimplantation genetic diagnosis (PGD), and somatic cell nuclear transfer (SCNT) technologies, have all been established and widely used in industrialization. However, as research continues to deepen and target species become more advanced, embryo engineering technology has also been developing in a more complex and sophisticated direction. At the same time, the success rate also shows a declining trend, resulting in an extension of the research and development cycle and rising costs. By studying the existing embryo engineering technology process, we discovered three critical nodes that have the greatest impact on the development of oocytes and early embryos, namely, oocyte micromanipulation, oocyte electrical activation/reconstructed embryo electrofusion, and the  To solve this problem, embryo engineering technology aimed at improving the efficiency of in vitro development (IVP) has been developed.Oocytes are formed by two meiotic divisions of oogonia. In the natural fertilization state, when the sperm passes through the uterus and fallopian tubes, the layer of disabling factor on the surface of the sperm secreted from the epididymis is removed; thus, the sperm has the capacity to fertilize an ovum. The sperm contacts the zona pellucida and binds to the sperm receptor (ZP3). Under the action of acrolein and N-amidase, the sperm nucleus and cytoplasm enter the cytoplasm of the oocyte through the zona pellucida. At this time, a large number of cortical granules in the outer cytoplasm below the oocyte membrane release their content into the perivitelline space, causing changes in the ZP3 glycoprotein molecules in the zona pellucida and causing the zona pellucida to lose its function of accepting sperm penetration. The reaction prevents the occurrence of multiple inseminations and polyspermy and ensures the biological characteristics of human monospermy. Under the stimulation of sperm penetration, the oocyte recovers and quickly completes the second meiosis II, forming a mature oocyte, and the second polar body is discharged into the perivitelline space. Since then, the male and female pronuclei are formed separately, and chromosomes are replicated at the same time. The double pronuclei move closer to the middle and merge to form a diploid zygote, and the fertilization process ends. Therefore, the number, morphology and mobility of live sperm and normal oocyte development are important conditions for ensuring fertilization. When one of these conditions is not met, normal fertilization cannot be completed in vitro fertilization (IVF) in vitro environment, various new technical requirements for micromanipulation have been proposed. The new technologies themselves have become increasingly sophisticated and complex, and the difficulty of implementation has also been increasing. The declining success rate has become the biggest problem facing the development of embryo engineering technology. Similarly, in the case of cloned animals, the differentiated somatic cell nucleus is transferred into the enucleated oocyte to reprogram the chromatin of the somatic cell, and then the embryo develops into a complete individual. As an important developmental biology research method, it has great potential for use in many aspects, such as agricultural breeding, biomedicine, and cherished animal protection. Somatic cells can achieve pluripotency through SCNT. However, due to various epigenetic barriers  in reprogramming, which makes somatic cell reprogramming incomplete, there is an extremely low potential for nuclear transfer embryo development. For example, the success rate of somatic cell cloning technology ranges from 5% for cattle The rapid development of embryo engineering technology has facilitated the leapfrog development of life sciences and reproductive medicine. Gene editing technology Figure 1, we discovered three high-risk nodes in the embryo engineering technology process that have the greatest impact on the development of oocytes and embryos, namely, oocyte micromanipulation, oocyte electrical activation/reconstructed embryo electrofusion, and early embryo in vitro culture. In the whole process of embryo engineering technology, the survival rate of oocytes passing through these three critical nodes to develop smoothly to the next stage is low. Therefore, how to help oocytes pass through these high-risk nodes has become a key issue that needs to be resolved. At present, scientists have carried out much research on related aspects.As shown in This paper focuses on the development of embryo engineering technology. The second to fourth sections of this paper describe the work that scientists have done to date in terms of the three high-risk nodes mentioned above and explain how the application of new techniques can improve the efficiency of embryo engineering technology and the survival rate through these high-risk nodes. In conclusion, the fifth section summarizes the current research status and challenges of these three critical nodes and proposes the prospect of future development directions. The fifth section analyses the research status and existing bottlenecks of these three critical nodes and proposes corresponding solutions and prospects for future development directions.First, in the process of embryo engineering technology, micromanipulation is the first hurdle that oocytes will confront. At present, the main micromanipulations on oocytes include: The sperm tail is trimmed and the sperm head is injected into the oocyte during ICSI; The nucleus of the oocyte is extracted and a new nucleus is injected during SCNT; One cell of the 4~8-cell stage embryo or the first/second polar body of the oocyte before or after fertilization is taken to detect whether the gene is defective during PGD. These micromanipulations on oocytes involve the modification and reorganization of the internal structure and material of the oocyte, such as performing a complex “surgery” on the oocyte, which is the most damaging step to the oocyte. It is also the most critical step in determining the survival rate. The accuracy of the manipulation part, the control of the extraction/injection quantity, and how to minimize the damage to the oocytes in the process of breaking through the zona pellucida are three critical problems that we must face. We summarize these issues as follows: targeted micromanipulation, minimally invasive micromanipulation and quantitative micromanipulation. From these three perspectives, researchers have carried out substantial work.Targeted micromanipulation is the precise manipulation of specific parts of the oocyte. To achieve this goal, it is first necessary to identify the internal structure of the oocyte through microscopic image identification technology to find the best operating position and operating angle, adjust the oocyte to the best position suitable for manipulation and fix it to facilitate the next manipulation.The identification of the oocyte and the end-effector is the first problem that needs to be solved to realize targeted microinjection. The traditional method is to magnify the target area through a microscope, and then the observer visually finds and identifies the oocytes and end-effectors Figure  shows the principle of EICP tracking. The first row shows the gradual closing process of the fixture, the middle row is the cumulative graph of events, and the last row shows the edge tracking of the gripper by the EICP model. However, the implementation of this algorithm requires the use of additional dynamic vision sensors on the hardware, which is relatively complex in terms of system integration. Anis et al. Figure  shows the recognition of the tips of micropipettes and microgrippers using this algorithm. Figures (a) - (c) depict the original image of the micropipette, corresponding MHI of the moving micropipette, and tip detection result based on active contour, respectively. Figures (d) - (f) show the original image of the microgripper, corresponding MHI of the moving microgripper, and tip detection result based on active contour. The algorithm can accurately locate the end-effector in the defocusing state or not in the image field, but the threshold in MHI detection must be selected appropriately to balance the algorithm failure rate and manipulation efficiency, and the selection of the threshold is sensitive to light. In addition, algorithms such as support vector machines The image identification of the end-effector realizes the precise control of the end-effector by the computer and the mechanical arm The image recognition of oocytes distinguishes the different organelles of oocytes to perform precise manipulations. The structure of oocytes is complex and includes dominant structures such as the zona pellucida, cell membrane, cytoplasm, and polar bodies, and recessive structures, such as the cytoskeleton, spindle, and mitochondria. Dominant structures can be observed directly under an optical microscope, while recessive structures can only be observed with the help of special techniques.Figure  shows how the vision system captures and processes images. Among them, figure (a) is the original image of the zebrafish embryo, figure (b) and (c) are edge detection results of the original image, and the final figure (d) is the histogram of the zebrafish embryo radius.The recognition of oocyte contours mainly uses image segmentation technology and contour extraction technology. Image segmentation is usually performed before contour extraction to make the contour of the image more prominent Figure , figure (a) is the original cropped image, figure (b) is the apply greyscale image opening, figure (c) is the Otsu's thresholding of (b) and Sobel edge detection of (a), figure (d) is the morphological filter and hole filling, figure (e) is the contour of the intracellular structure and its inscribed circle in yellow and blue lines, respectively, figure (f) is the overlapping part of the ring of the inscribed circle and the intracellular structure contour indicated in green, figure (g) is the ellipse fitting based on the overlapping part (the red ellipse is used to estimate the portion of cytoplasm), figure (h) is the estimated cytoplasm from (d) and figure (i) is the largest suitable blob is taken as the polar body after the binary image open operation. Mozafar et al. The image identification of the oocyte polar body is mainly used to rotate the oocyte to a proper posture to avoid the polar body during microinjection. Leung et al. Figure ; the fusion result is on the left, and the edge information of the fusion result is on the right. The fused image can clearly observe the spindle in the oocyte without losing the information of the operating environment.The purpose of image identification for oocyte spindles is to extract the nuclei of oocytes precisely and improve the success rate and efficiency of experiments. The traditional method is mainly to observe the spindle through the fluorescent probe method Table 1 summarizes the current common contact and noncontact oocyte operating and controlling methods.After accurately identifying the internal structure of oocytes, the operating position and operating angle were determined. Since an ordinary optical microscope can only provide two-dimensional image information and the operating space is limited, the traditional way to move the end-effector to the right position is not suitable. However, it is a better choice to adjust the oocyte to the most suitable posture and then fix it through various techniques; this approach is called oocyte manipulation. In the process of oocyte micromanipulation, the techniques for adjusting oocyte posture can be classified into contact methods and noncontact methods. The contact methods mainly fix the oocytes through negative pressure holding or microtraps and then adjust the oocytes to a proper posture by operating tools. Noncontact methods involve changing the position and posture of oocytes through noncontact techniques such as microfluidics, dielectrophoresis, optical tweezers or magnetic fields and then fixing them. Figure , Huang et al. Figure , Sun et al. The traditional method is to attach a microcapillary to an air pump, which generates negative pressure to suction oocytes Figure , when Sun et al. Figure . A direction control algorithm based on the movement of oocytes in the microchannel is proposed. Visual tracking of the polar body is used to provide information about the embryo direction. The experimental results show that the embryo direction can be automatically controlled without manual intervention. Conventional microinjectors can access the device through the lumen so that fixed oocytes can be manipulated.In noncontact oocyte manipulation technologies, microfluidics mainly uses microfluidic flow to generate forces acting on oocytes, thereby fixing oocytes or adjusting their poses Figure , Huang et al. Figure , Yu et al. Figure , Huang et al. Dielectrophoresis (DEP) refers to the process in which oocytes are polarized in nonuniform electric fields and subjected to dielectrophoretic forces. Thus, oocytes can be controlled by adjusting electric field parameters to change the dielectrophoretic forces Optical tweezers provide a new method for cellular manipulation The thick zona pellucida outside the oocyte becomes the barrier we need to break through after determining the precise operating position of the oocyte and adjusting the posture of the oocyte to meet the end-effector. Manual manipulation mainly reduces the damage to oocytes through thinner microcapillary and higher puncture speed. However, if the needle is too thin, it will be too soft and will affect the suction or injection of substance. Therefore, the diameter of the needle tip cannot be reduced indefinitely. To reduce the damage to oocytes and evaluate the damage to oocytes in the process of membrane puncturing, many new technologies have been applied. Researchers first believe that the force received by oocytes during membrane puncture is an important factor for oocyte injury and hope to measure and control the micropuncture force. At the same time, some advanced membrane puncture technologies have also been used to improve oocyte micromanipulation devices.Table 2, the micropuncture force measurement methods are mainly divided into direct and indirect measurements In the oocyte membrane puncturing experiment, the micropuncture force exerted on the oocyte is usually on the order of micronewtons. As shown in Figure , Beutel et al. Figure , Beyeler et al. Figure  shows the injection force sensing scheme designed by Xie et al. Figure , Zhang et al. The resistance of the piezoresistive force sensor will change with the variation in the external force, and the size of the external force can be obtained by measuring the resistance Among the indirect measurement methods, the vision-based method mainly uses microscopic image processing and an accurate oocyte mechanics model to determine the micropuncture force Figure , the proposed force control framework includes two control loops. The inner loop is an impedance control used to specify the interaction between the microcapillary and the embryo. The outer loop is a force tracking nonlinear controller using feedback linearization technology. With the proposed force control method, the puncture force can be clearly adjusted during the embryo injection process to follow the desired force trajectory. Karimirad et al. µN to mN. The algorithm can trace and characterize embryo deformations by extracting geometric features called “pit angles” directly from images of the embryo micromanipulation process. The neural network was trained with the experimental data of zebrafish embryo micromanipulation, and the trained neural network was suitable for indentation of any other spherical elastic object. The results of this study can be used to measure forces during microinjection of biological cells, such as mouse oocytes/embryos, and are particularly suitable for situations requiring force feedback. Sun et al. Figure , by introducing the weight coefficient method, the challenge of transient pulsation between force and position controller switching can be reduced. An adaptive sliding mode control method with parameter estimation is used to compensate for the hysteresis nonlinearity and disturbance of the piezoelectric actuator. The injection force is controlled by an incremental PID controller. The whole system is low cost, easy to install and easy to maintain.The control of the micropuncture force is very important in oocyte membrane puncturing experiments. If the micropuncture force is too small, then it will not be able to penetrate the zona pellucida and cell membrane of the oocyte. If the injection force is too large, then the oocyte may be hurt or even die. Therefore, achieving the precise control of the injection force has become the key to improving the success rate. Xie et al. The conventional microinjector penetrates the zona pellucida and cell membrane of oocytes by mechanical squeezing. During the puncture process, a certain force and initial velocity must be given to the microcapillary tip Figure , Huang et al. Figure , Johnson et al. Figure , Dai et al. To reduce the lateral vibration of the piezoelectric ultrasonic microinjector, researchers began to improve its structure. As shown in At present, a great deal of work has been carried out in the study of oocyte injury. However, in terms of reducing oocyte injury during microinjection, these previous studies still have deficiencies. First, research on oocyte injury has basically focused on the cell surface, such as the zona pellucida. However, there are a large number of skeletal structures inside the cell, and the injury of these structures cannot be ignored. At present, relevant studies have established a more detailed mechanical model of oocytes to study and evaluate the mechanism of injury to cell internal fine and cytoskeletal structure during microinjection. For example, Liu et al. In micromanipulation, it is often required to implement material exchange inside or outside the oocyte through injection or extraction, which is mainly achieved by microcapillary connections with air or liquid pumps. When the microcapillary is inserted into the oocyte, the positive/negative pressure of the air pump is adjusted to push/suck material into/outside the oocytes through the microcapillary Figure , by connecting the buffer tank to the gas channel, the new pump system has higher stability and resolution, various positive and negative pressures between -30 kPa and +150 kPa can be generated, the pressure control accuracy is less than 1 Pa, and the precision of the injection volume can reach 0.0022 pl. Noori et al. Figure . The advantage of using electroosmosis for reagent transport is that it eliminates the need for dedicated pumps and does not limit the size of the microcapillary, providing true scalability for the microcapillary. In addition, electroosmosis provides nonpulsed flow and precise electrical quantity control.Microinjection experiments are usually carried out on commercial injection pumps using an air or oil pump as a pressure source. Commercial injection pumps include CellTram 4r Air and CellTram 4r Oil (Eppendorf), IM-400 and IM-400B (Narishige). However, due to the instability and accuracy of the pressure source, commercial pumps have some problems with respect to picolitre level injection, and the success rate varies greatly between different operators. Li et al. Zhao et al. The oocyte micromanipulation node is the most complicated node in the embryo engineering technology process and the node that causes the greatest damage to the oocyte. The quality of its completion directly affects the subsequent nodes. Therefore, much research has been focused on this node. At present, researchers have been trying to replace traditional manual operations with machine operations, and machine-assisted oocyte micromanipulation has been developing towards a targeted, minimally invasive, and quantitative direction. The application of the automatic identification and positioning technology of oocytes and end-effectors has greatly improved the degree of the automation of oocyte microinjection. The application of various contact and noncontact control methods has realized the control of oocytes with precise posture adjustment and fixation. The emergence of piezoelectric ultrasonic microscopic rupture technology makes it possible to complete the rupture of oocytes with minor damage to the oocytes. The application of various microinjection force sensors and control technologies makes it possible that the injection force is known and controllable. The development of high-precision syringe pumps and the closed-loop control technology of injection dose allow the exchange of materials inside and outside the oocytes to achieve nanoliter-level precision. These technologies use the machine's high sensitivity, high precision, and high stability to compensate for the lack of manual operation stability to improve the efficiency and success rate of micromanipulation. Although these technologies have shown certain advantages in terms of the degree of automation, in terms of the core index success rate, the results of machined micromanipulations have not shown much advantage over manual operations. The addition of various complex advanced technologies also increases the complexity of the system and increases the difficulty of operation for actual operators. This is also the inherent reason why machine-assisted micromanipulation technology still cannot replace manual operation and cannot be widely promoted and applied. Therefore, how to demonstrate the technical advantages of machine-assisted micromanipulation technology in the core index success rate will be a problem that we need to seriously think about and solve.In the process of embryo engineering technology, oocyte electrical activation/reconstructed embryo electrofusion is the second critical node that oocytes will confront. Various bioelectrical signals play an important role in the natural development of oocytes. Bioelectric stimulation can change the oocyte zona pellucida and the movement of ions in the solution to achieve various effects. Electrical stimulation is also a common method in various stages of oocyte micromanipulation. For example, during natural fertilization, oocyte activation is accomplished by sperm entry and induced calcium oscillation. In the process of parthenogenetic activation (PA) and ICSI, the artificially assisted activation of oocytes is essential. Presently known artificially assisted activation methods include mechanical stimulation 2+. On the one hand, the increased Ca2+ content destroys the existing cytostatic factor (CSF), thereby reducing the activity of the maturation promoting factor (MPF). On the other hand, increased Ca2+ causes the degradation of cyclin sensitive to Ca2+, initiates the activation of oocytes, and prompts oocytes to leave the MⅡ phase to complete embryonic development after meiosis. Electrofusion refers to the use of electrical pulses to create recoverable micropores in the recipient cell and donor cell in the fusion solution so that the two cell membranes fuse with each other. If the electric pulse is too large, then the micropores in the cell membrane will not be repaired. Thus, an appropriate electric pulse can make the cell membranes fuse, and the cytoplasm of the cells can facilitate communication and finally merge into one cell. Both electrical activation and electrofusion are the principles for establishing electroporation. In essence, the phospholipid bilayer structure of the cell membrane changes under the stimulation of electrical pulses. Electroporation refers to the generation of micropores in the cell membrane under the action of a high-intensity electric field, which improves the permeability of the cell membrane Electrical stimulation techniques related to oocytes are mainly divided into electrical activation and electrofusion. Electrical activation means that the oocyte membrane is stimulated by a high-voltage DC pulse, and calcium ions flow into the oocyte as a second messenger through ion channels on the cell membrane, causing changes in the chemical substances in the oocyte, that is, stimulating the endoplasmic reticulum in the oocyte. The stored calcium pool is released, thereby increasing the concentration of CamV is given by formula (3-1):When calculating the membrane potential difference, the cell membrane is regarded as an insulator, and the cytoplasm inside the cell and the suspension outside the cell are both regarded as electrolyte solutions. Under the action of an external electric field, both the cytoplasm and the cell suspension are polarized, and a membrane is produced. The potential difference extE is the applied electric field intensity, θ is the angle between the radial direction at an arbitrary point on the membrane and the electric field direction, and τ is the relaxation time constant of the dielectric membrane. When the frequency of the applied alternating electric field is low enough, we assume ωτ << 1, or when the applied electric field is a direct current (DC) electric field, formula (3-1) can be simplified to formula (3-2):where θ = 0° or 180°, the membrane potential difference induced by the applied electric field is further simplified as Eq. (3-3):With two cellular extremum points, crV = V. Under this condition, the induced membrane potential crV is called the critical membrane potential, while the applied electric field crE is called the critical applied electric field. When the intensity of the applied electric field is higher than crE, the cell membrane begins to perforate. The higher the applied electric field intensity is, more or larger holes occur on the membrane. However, if the applied electric field is too strong, cell perforation becomes irreversible, and cells can be permanently damaged.The potential difference is the largest at these two points. The induced membrane potential increases with increasing applied electric field intensity. In cell electroporation, the cell membrane will be perforated when the intensity reaches a special value: The above is the theoretical basis of cell electroporation, but the actual situation is much more complicated than this. Virtually all cells are not ideal spheres, different cells vary in size and shape, and the composition of their membranes and media varies. Therefore, when performing cell electroporation experiments, the theoretically calculated values are only for reference, and the best parameters are obtained through continuous experiments. Since cells have a strong ability to repair themselves, they can withstand large pulse amplitudes. Scientists have found that even low amplitude pulses can cause electroporation, but stronger amplitude pulses can achieve higher electroporation efficiency. The lower limit of the pulse width must be longer than the charging time of the membrane and the elastic recovery time of the membrane to keep the pores open. There is a complementary relationship between the pulse width and amplitude. To reduce the pulse amplitude, the pulse width needs to be widened to compensate; the efficiency of perforation is often proportional to the product of the pulse amplitude and the width. The most commonly used pulse waveforms are DC square wave pulses and exponentially decayed resistance-capacitance (RC) pulses. If the electrical pulse can maintain the membrane pores open for a period of time with a lower voltage after breaking through the cell membrane, then it will help improve the efficiency of cell electroporation. Therefore, RC pulses are often more effective than DC pulses of the same amplitude. In addition, increasing the number of pulses can obviously increase the efficiency of cell electroporation, but the death rate of cells also increases.Table 3. By analysing the experimental data of previous researchers, we found that when parthenogenetic activation of oocytes is carried out, the effect of electrical activation (EA) is obviously better than that of chemical activation (CA) and mechanical activation (MA), and the combination of EA and CA can obtain a better effect.Researchers have used electrical stimulation to activate oocytes when studying PA and have carried out experimental exploration on many organisms, such as pigs Table 4. By analysing the experimental data of previous scientific researchers, we found that electrical activation can significantly increase the fertilization rate and cleavage rate of oocytes after ICSI, and the electrical activation of oocytes can be applied before or after ICSI.Electrical stimulation is also widely used in ICSI to stimulate oocytes. Experimental studies have been conducted on many animals, such as rabbits Table 5. By analysing the experimental conclusions in Table 5, we found that appropriate chemical treatment of oocytes after electrofusion can improve the success rate of fusion. Generally, the electric field intensity required for electrical activation of oocytes should be lower than the electric field intensity required for electrofusion. However, when analysing the experimental data in Table 4 and Table 5, we found that this conclusion was not obvious, which may be due to the various oocytes used in the experiment, the composition of the culture medium, and the electric activation/electrofusion device being different.In SCNT, the fusion of somatic cells and enucleated oocytes usually uses electrofusion. Experimental studies have been conducted on many animals, such as pigs Traditional cell electrofusion systems use a fusion chamber with two parallel plate electrodes for electrofusion. The distance between two parallel plate electrodes is 1~10 mm, and the fusion electric field is generally ~kV/cm, which makes the voltage applied to the parallel plate electrodes reach hundreds to thousands of volts. Due to the surface tension of the liquid and the electrochemical reaction between the electrode and the buffer solution to produce bubbles, the electric field distribution in the electrofusion chamber is not uniform. In addition, the posture of the cells between the parallel plate electrodes is difficult to unify, causing fluctuations in the effect of electrical stimulation/electrofusion. Due to the limitations of traditional electrofusion systems, the effect of electrical stimulation/electrofusion on cells is very rough. Even the same device, the same batch of oocytes, and the same electrical parameters have different effects. Therefore, the miniaturization and refinement of oocyte electrical stimulation/electrofusion devices is an inevitable trend.Figure 11, the experiment used three kinds of microelectrodes: 15 μm tip-end, 100 μm frustum-end and 200 μm parallel microelectrodes. These microelectrodes were combined into four groups for the experiment: tip-end plus tip-end (TT), tip-end plus frustum-end (TF), frustum-end plus frustum-end (FF) and parallel microelectrodes (PM). In the four schemes, the interface between the nuclear donor and oocyte was perpendicular to the electric field. When the electrodes did not apply pressure to the reconstruction configuration, the fusion rate of the four groups was not significantly different; all were approximately 74%. When pressure was applied, the TT group showed a higher fusion rate (94.9%) and a lower degradation rate (2.1%). Compared with traditional electrode chamber fusion, electrofusion with pressure applied to the tip needle microelectrode is a better SCNT electrofusion scheme.Liu et al. Figure , Guillaume et al. Figure , the microfluidic chip has 6 microchambers and a total of 1368 pairs of high-aspect-ratio silicon microelectrodes. The microchannels were 20 μm deep and 80 μm wide, and the electrode spacing was 50-100 μm, with increments of 10 μm. In the experiment, a 1.2 kV/cm, 2 MHz alternating current was used to pair the plant protoplasts; several 4 kV/cm, 20-50 μs direct current pulses were used for electrofusion. Hu et al. Figure , 2.2×104 microelectrodes were integrated on the chip, which can perform cell electrofusion in large quantities. Human embryonic kidney cells (HEK-293) were paired under a 6 V, 1 MHz alternating current, and electrofusion was performed under 3 to 5 direct current pulses of 35 V for 50 μs.At the same time, under the premise of ensuring the success rate of oocyte electrical activation/electrofusion, microfluidic chips have inherent advantages in improving the efficiency of electrical activation/electrofusion. Although no related research on oocyte electrical activation/fusion chips has been reported, there are indeed many studies on microfluidic electrofusion chips for somatic cells, which can be referenced for future development of high-precision oocyte electrical stimulation chips. Researchers first found that cell pairing can be achieved by using dielectrophoresis force. A kind of cell electrofusion microfluidic chip with interdigitated microelectrodes was developed Figure , interdigital titanium electrodes with a thickness of 300 μm, a width of 250 μm, and a pitch of 250 μm were prepared on a borosilicate glass substrate. The electrode was coated with an electrically insulating photosensitive epoxy polymer (SU-8) film with a thickness of 4 or 22 µm. Micropits with diameters of 10-80 µm were made above the electrode. During the experiment, it was necessary to manually load the donor somatic cells and the recipient oocytes, draw the cells into the micropits through DEP to achieve pairing, and then apply high-voltage direct current pulses for electrofusion. The device cleverly introduces the micropit structure onto the microfluidic electrofusion chip, which effectively improves the success rate of cell pairing. However, DEP is still needed to fix cells, and the manual loading of cells undoubtedly increases the cumbersomeness of the experiment and reduces the efficiency. Skelley et al. Figure , the microtraps used to capture cells consist of a larger frontal capture ring , a smaller dorsal capture ring , and a support ring . In the experiment, a single cell A (green fluorescence) was first captured with a smaller backside capture cup. Once the array was saturated, cell A was transferred directly “down” to the relatively large capture cup. Finally, cell B (red fluorescence) was loaded. Cell B was captured by the larger capture ring in front and paired with cell A, with a success rate of up to 70%. This design makes clever use of hydrodynamics and specially designed microtraps to capture and pair cells. Robinson, T et al. Figure , the chip contains a three-layer structure: a circular ring valve is integrated on the pressure control layer, a PDMS trap for capturing target cells is integrated in the fluid layer, and a patterned electrode is integrated on the glass base. GUVs with diameters much larger than 20 μm (the size of the trap passage) will not enter the trap. GUVs with diameters less than 8 μm will escape through a gap in the trap, allowing them to capture target-sized GUVs and pair them up. After all traps are captured and paired, the ring valve in each chamber can be hydraulically driven to isolate each trap to prevent GUVs from being affected by fluid shear. Finally, the electrofusion of GUVs can be completed by the output of a DC pulse through the microelectrode. Sugahara, K et al. Figure , the chip structure is very simple and was prepared from polydimethylsiloxane (PDMS) using standard soft lithography. One side of the snake-like pathway in the centre of the chip is designed to trap cells, and the chip is flanked by electrodes. The GUV suspension was injected into the chip with a syringe pump at a flow rate of 400 nL/min. GUVs were captured when they flow through the trap on the side of the channel. The trap can capture two GUVs and complete the pairing. The subsequent GUVs will follow the bypass channel and continue to flow until caught by the next trap. After the pairing is completed, the GUVs are electrically fused with DC pulse input through the electrodes. However, it should be noted that neither the microfluidic fusion chip with an interdigitated electrode nor the microfluidic fusion chip with a “microtrap” has been successfully applied to oocytes. In the future, a microfluidic electrofusion chip more suitable for oocytes may be developed.Compared with traditional electrofusion systems, microfluidic chips containing interdigitated microelectrode arrays have the advantages of a low voltage, a simple structure, and a high throughput. However, when using the dielectrophoretic force generated by alternating current to pair the cells, the number of paired cells cannot be controlled, and the cells are easily trapped in the nonelectrofusion zone between the two adjacent electrodes, which greatly reduces the pairing efficiency of the cells. Therefore, it is a better choice to use microstructures in microfluidic chips for cell pairing. Clow et al. Tables  of section 3.2 are totally different. Therefore, to truly determine the law of electrical activation/electrofusion parameters, the miniaturization and refinement of experimental devices is an inevitable trend. To date, substantial research and application in somatic cell electrofusion with precision electrical stimulation technology based on microfluidic chip technology has been carried out. This technology has shown good technical advantages and will definitely be the future development trend for oocyte electrical stimulation technology.The oocyte electrical activation/reconstructed embryo electrofusion node is the weakest part of the embryo engineering technology process. Both electrical stimulation strategies and devices need to be improved. To date, much research work has been performed by researchers with biological backgrounds, focusing on adjusting various “optimal” electrical stimulation parameters to obtain a high activation rate/fusion rate. However, in many studies, only irregular conclusions have been obtained regarding the optimization parameters under the existing crude electrical stimulation device. This is also the underlying reason why the “optimal electrical stimulation parameters” obtained by relevant studies shown in in vitro culture of early embryos is the third key node that determines the ultimate success rate. In the natural development process, the nutrition of the early embryo comes from the secretion of the mother's oviduct and uterine cavity. Its cellular activities  are affected by factors produced by the environment, the embryo itself, and the cells of the reproductive tract. With the continuous development of biotechnology, we can bypass the fallopian tube and transfer embryos that have developed to the blastocyst stage to the uterus for further development through in vitro culture. The development process of embryo culture technology in vitro essentially involves constantly approaching the natural embryonic development process, simulating the natural environment of embryo development and meeting changing nutritional needs during embryo development. Chemical and physical factors have been proven to be a prerequisite for improving the culture environment of preimplantation embryos and are the basis for improving the rate and survival of blastocysts. At the same time, the microenvironment and macroenvironment in which the embryo is cultured in vitro also play a vital role in the development of the embryo. This part starts with analysing the reproductive mechanism and influencing factors of early embryos and deeply analyses the complex environment of early embryo development. Then, from the perspective of engineering technology, the research progress of in vitro embryo culture technology was systematically introduced.In the process of oocyte micromanipulation, the The reproductive mechanism is a complicated problem of the interaction between fluid and structure. The sperm and oocyte meet to form a fertilized ovum, the fertilized ovum further develops to the cleavage and blastocyst stages, and the process of blastocyst transport to the uterus is completed in the fallopian tube. The fallopian tube plays an important role, and the whole reproductive process undergoes dynamic changes under mechanical and biochemical conditions in vitro. Chemical and physical factors must be considered when early embryos are cultured in vitro. Table 6 summarizes the effects of common chemical and physical factors on embryos.After understanding the reproductive mechanism of early embryos, researchers have attempted to create a culture environment close to living body development for early embryos when they are cultured in vitro growth factors. These growth factors composed of proteins usually bind to factor-specific receptors on the cell surface and affect tissue growth and differentiation In vitro, the growth factors secreted by monolayer cells can promote the maturation of oocytes and the development of early embryos. Co-cultured cells can release a variety of different growth factors in the culture medium, which can improve the development of co-cultured embryos. In addition, the co-culture method is beneficial for the embryonic development of some mammals  culture method [204.205], GO (glass oviduct) culture method in vitro cell, tissue and embryo culture. Using alginate hydrogel as the test material, Zhao et al. in vitro fertilization and parthenogenesis in an alginate encapsulation culture system (AECS), an alginate overlay culture system (AOCS) and a control culture system . Finally, it was found that the AECS system and the AOCS system can promote the cell proliferation, elongation and differentiation of bovine embryos.In conventional culture methods, oil is usually an indispensable material that can prevent the medium from evaporating, protect the medium from pollution in vitro culture methods are still the mainstream in vitro culture methods in practical applications. These methods mainly consider the chemical and physical conditions during early embryonic development. However, its shortcomings are also very obvious. The developmental process of early embryos is a dynamic process. There are complex interaction processes between embryos and the tissue environment and even embryos. Long-term static culture is not a good choice; at the same time, due to the static state of the culture medium, the repeated manual pipetting and washing of embryos are required. Additionally, drastic changes in the temperature, pH, osmotic pressure, light and mechanical stress of the embryo environment during this process may adversely affect embryo development. At the same time, this process also has very high requirements with respect to the experience and technology of the experimental operators, and the success rate between different operators is very different.To date, conventional in vitro culture process to improve the success rate of an in vitro culture and to minimize the interference of human manipulation factors on the results, researchers have begun to develop various standardized and automated pseudo-fallopian tube/uterine dynamic culture systems. In addition to providing suitable physical and chemical conditions for early embryos, the pseudo-tubal/uterine dynamic culture system creates a dynamic environment close to the natural development of the living body on the basis of studying the reproductive mechanism. The early dynamic culture system was mainly a mechanical stimulation culture system in vitro and subjected to similar mechanical stimulation in the mother. For example, Koji Matsuura et al. To approach the fallopian tube/uterine environment as much as possible during the Figure , the 200-micron-deep wells reduced the maximum flow velocity near the oocytes by 97%; at the same time, the 200-micron-deep microwells were more capable of capturing and removing debris than the other three depths. Stephanie et al. Microfluidic technology is very consistent with the physiological requirements of the microenvironment required for embryo cultures. Microfluidic systems have been reported for embryo cultures under static and dynamic conditions. In a static culture, embryos are placed in a microchannel or culture chamber and cultured without a flowing medium. Under dynamic conditions, embryos are cultured in a flowing medium to simulate their physical environment in the body. Han et al. -3 dyn/cm2, and the average shear force per unit area of the microfunnel is 3.55×10-3 dyn/cm2. Compared with static culture, dynamic funnel culture significantly improved the blastocyst rate and pregnancy rate, as shown in Figure .Microfluidic technology creates a dynamic environment through the flow of culture media to simulate the fluid force and mechanical stimulation of the embryo in the oviduct environment. Miyoshi et al. in vitro simulates the physical and chemical microenvironment. The typical medium composition is based on basic nutrients and some specialized factors, which are randomly mixed in a static culture environment. Cytokines and endocrine signals at the cell-cell and tissue levels are not integrated into the signalling pathway. Xiao et al. Figure . Gametes and early embryos are in close contact with epithelial cells when they are transported in the oviduct. Previous research on the in vitro culture of epithelial cells mainly focused on the monolayer culture of epithelial cells and the culture of fallopian tube tissue explants. Explant culture mainly uses epithelial cells to form ciliated beating vesicles. In monolayer culture, epithelial cells rapidly polarize and differentiate, becoming flat cells that lose their cilia and secretory ability.The method of culturing mammalian cells in vitro culture between embryos. Michelle et al. in vitro. Xie Lan et al. in vitro fertilization is integrated into the device, including ovum positioning, sperm screening, fertilization and other steps. An array of 4×4 cells is designed. Each cell is a circular fence structure composed of 8 pillars. The inner diameter of the cell is 400 microns to capture 1 to 4 oocytes, and the gap between the pillars is 50 microns to facilitate the passage of sperm, as shown in Figure . Connecting the 4 linear channels with the oocyte positioning area can effectively screen sperm, quickly replace the culture medium, remove unattached and dead sperm, and provide a gentle and rapid medium replacement method. The damage to the embryo caused by the embryo from one droplet to another is avoided. Through dynamic monitoring of embryo development, the microfluidic chip can successfully perform in vitro fertilization, and the growth rate of embryos is similar to that of conventional in vitro fertilization (p>0.1).At present, there are some microfluidic devices combined with pseudo-tubal dynamic culture and co-culture systems. Paula et al. Figure . Furthermore, Ferraz et al. Figure . Chang et al. Figure . A semicircular arc-shaped microstructure was designed on the side wall of the culture chamber. The embryo uses the microstructure to generate mechanical stimulation to the embryo under the drive of fluid, which simulates the cilia of the embryo in the fallopian tube. In addition, the chip simulates hormone changes during the menstrual cycle and creates a gradient channel that automatically generates different concentrations of steroid hormones. In addition, multiple culture chambers are connected to each other through microchannels to produce a group culture effect, which is conducive to the exchange of autocrine and paracrine factors between embryos. In the culture room, endometrial stromal cells are cultured on the porous membrane, and fresh culture fluid is delivered to the cells on the top and bottom, effectively avoiding the adverse effects on the embryo caused by apoptosis. Experimental results showed that the blastocyst development rate of 67±10.05% under the co-culture condition was higher than that of 49±8.19% under the non-co-culture condition. The co-culture condition was more conducive to the culture and development of the culture. In short, with the future development of microfluidic technology, the integration of co-culture technology with new culture materials and culture devices can create an in vitro culture environment closer to that of a living body for early embryos and promote the development of in vitro culture technology for early mouse embryos.Ferraz et al. in vitro, laminar flow microfluidic devices mainly provide a microchannel, microstructure and continuous flow culture liquid, while fluid mechanics technology is used to manipulate cells and simulate the physiological microenvironment experienced in vivoWhen Figure . The DMF platform can transport oocytes or embryos to a designated location for in vitro fertilization of oocytes or the chemical and mechanical treatment of embryos. Derek G. Pyne et al. Figure , including droplet delivery, mixing, distribution and splitting. Mouse embryos were placed in droplets that function as capillaries, and a single mouse embryo was transported through the DMF platform during the complete vitrification process. The embryo survival rate and development rate obtained by this device are equivalent to those obtained manually. This method has the advantages of automatic operation, the generation of a cryoprotectant concentration gradient and the feasibility of loading and retrieving embryos. Huang et al. Figure , to cultivate mammalian embryos in a single droplet. Using the EWOD system to provide a dynamic fluid environment, this device provides a discontinuous microfluidic environment to simulate the in vivo embryonic development environment, promoting mouse embryo cleavage and the development to the preimplantation stage. However, this system cannot provide stable embryo movement speed; that is, the embryo movement speed in the droplet is random. Huang further designed a new type of circular DMF chip Figure , with the functions of in vitro fertilization and in vitro culture. In the experiment, HTF droplets containing male and female mouse gametes were mixed with DMF-treated droplets to form fertilized ova. The fertilized embryos were dynamically cultured and transported between electrode pairs at a certain interval. The in vitro fertilization rate of the DMF platform was 8.7% higher than that of traditional petri dishes, and the number of embryos that have developed to 8-cells was similar. The ring-shaped DMF chip solves the problem of gas exchange during embryo culture. Embryos in dynamic culture develop faster than those in static culture.The EWOD (electrowetting on dielectric) system is a droplet driving mechanism in the DMF platform and is one of the most advanced methods for manipulating droplets. The EWOD system is derived from the electrowetting phenomenon that changes the contact angle of the liquid. By applying an AC voltage to the electrode directly below the surface of the substrate, the wettability of the droplet surface is changed locally, and the attraction that drives the movement of the droplet is generated. It is usually quantified by the degree of contact angle reduction. The change in the contact angle in the EWOD system is generally defined by the Young-Lippmann equation in vitro culture of early embryos, researchers have been constantly exploring the reproductive mechanism of early embryo development and have found that there are many complex factors affecting early embryo development. On the one hand, based on cost, operability and other factors, the conventional in vitro culture method of static culture still occupies a dominant position in many biological and medical laboratories. To improve this technology, some studies have introduced new culture materials to improve the culture effect and tap its potential. On the other hand, the reproductive mechanism of early embryos shows that dynamic culture technology is the most natural form of a culture system and will be the future development direction of in vitro culture technology. A large number of studies have focused on the tubal/uterine dynamic culture system. With the development of microfluidic technology, dynamic culture technology has been greatly promoted. By simulating organ-like structural microenvironments and dynamic biochemical/mechanical stimulation processes, various complex culture systems have been constructed to approach the natural development environment of early embryos, which has indeed brought about a substantial increase in the embryo development rate and has shown great advantages. There is no problem in the theoretical verification. However, considerable effort is required to replace traditional in vitro culture technology with practical systems. The system is too complex, the processing cost is high, and the reusability and ease of use are not strong. Cost reduction and moving these advanced technologies from the laboratory to industry are key problems to be solved in the future.In related research regarding the in vitro culture of early embryos. Researchers have started from these three critical nodes and developed numerous novel machine-assisted technologies to improve the effect. The main research content is shown in Figure 17. However, machine-assisted technology has never shown much advantage in terms of the core index, i.e., the success rate. This reason requires us to have a more in-depth understanding about the technical process of embryo manipulation. The embryo engineering technology process is first a life development process, with its own laws of life activities, and the operation technology without an in-depth analysis of its internal physiological mechanism cannot guarantee to maximize its advantages. Second, embryo operation technology is a sequential operation process, and its influencing factors are very complicated. All the factors of the previous operation node will have an impact on the following steps, and the improvement effect of some factors on a single node will be diluted by other nodes. In future related research, we should no longer pursue the ultimate improvement of certain aspects of technology in one aspect. Instead, we should have a systematic concept. First, we should find the weak bottleneck in the most critical nodes that affects the success rate to make breakthroughs, then take the process as a whole, conduct systematic analyses and repeated corrections, and finally achieve a substantial increase in the overall success rate of machine-assisted embryo engineering technology. The details are as follows:From the perspective of engineering, this article presents the three high-risk nodes that most affect the success rate of the entire process of embryo engineering technology according to its current status and development trend, namely, oocyte micromanipulation, oocyte electrical activation/reconstructed embryo electrofusion and In related research on oocyte micromanipulation, although researchers have invested substantial energy, they have tried to use the high sensitivity, high positioning accuracy, and high stability of machines to compensate for deficiencies in the stability of manual operation to achieve an improved success rate. However, the effect of machine-assisted micromanipulation is inadequate, and the reasons may be as follows. First, the existing micromanipulation technology lacks scientific theoretical guidance. The selection of existing micromanipulation strategies  is basically based on manual operation experience. Second, researchers generally regard the target oocyte as a cortical membrane model. Research on oocyte damage has basically focused on the zona pellucida, but the internal structures of oocytes are often ignored. However, these internal microstructures, such as the nucleus and skeleton, are more important to oocytes. In future research, a guiding strategy for robotic oocyte micromanipulation should be established to provide theoretical guidance for micromanipulation instead of continuing to use the empirical conclusions of manual manipulation. The evaluation mechanism of the degree of damage in oocyte micromanipulation should be established and fed back to the experiment to help find the best operation mode and parameters.In research on oocyte electrical activation/reconstructed embryo electrofusion, we should first consider the characteristics of oocyte electrical stimulation, combine microfluidic technology and micromanipulation technology, and develop a new precise electrical stimulation device suitable for oocyte electrical activation/reconstructed embryo electrofusion. Second, we should deeply reveal the physiological mechanism of oocyte electrical activation/reconstructed embryo electrofusion, study the effect of the different parameters of electrical stimulation signals on oocytes, and realize the optimization of electrical stimulation parameters. Through the combination of these two aspects, the best electrical stimulation effect can be obtained by trial and error correction.in vitro culture of early embryos, although researchers have constructed a variety of organ-like structural microenvironments and complex culture systems of dynamic biochemical/mechanical stimulation processes, they have brought a substantial increase in the rate of embryo development. However, there is still a long way to go for all kinds of systems to replace traditional in vitro culture technology. The existing systems are too complex, the processing cost is high, and the reusability and ease of use are not strong. To make it out of the laboratory, future work should focus on system improvement through the introduction of new processing technology, new materials and new structures, reduce the cost of products, strengthen modular design, increase the reuse rate of products and reduce the cost of consumables. Only in this way can actual operators use and promote the development of related industries.In the study of the in vitro culture link based on experience or image data, combined with the static morphological quality assessment system and the jet lag embryo culture detection system. Generally, this kind of evaluation system is very rough, empirical and subjective and is too strong to meet the developmental needs of embryo manipulation technology. Quantitative monitoring methods should be introduced for the entire process of embryo engineering technology, combined with complex models for predictive analysis, so that problems can be identified. Only by performing targeted improvements can machine-assisted embryo manipulation technology achieve a substantial increase in the overall success rate, show its superiority over manual operations, and promote technological upgrading. Ultimately, as a basic technology, this technology gives a considerable boost to related biomedical research and industry.In addition, although a large number of new technologies have been developed to improve the various nodes of embryo manipulation technology, how can we evaluate its effect? As the influencing factors of embryo engineering technology are too complicated, it is still very difficult to evaluate them. At present, research related to embryo engineering technology basically regards the embryo development rate/survival rate after surgery as the evaluation standard of the pros and cons of the new technology. In reproductive medicine, the final effect of the entire operation process can only be evaluated in the last


Image demosaicking has been an essential and challenging problem among the most crucial steps of image processing behind image sensors. Due to the rapid development of intelligent processors based on deep learning, several demosaicking methods based on a convolutional neural network (CNN) have been proposed. However, it is difficult for their networks to run in real-time on edge computing devices with a large number of model parameters. This paper presents a compact demosaicking neural network based on the UNet++ structure. The network inserts densely connected layer blocks and adopts Gaussian smoothing layers instead of down-sampling operations before the backbone network. The densely connected blocks can extract mosaic image features efficiently by utilizing the correlation between feature maps. Furthermore, the block adopts depthwise separable convolutions to reduce the model parameters; the Gaussian smoothing layer can expand the receptive fields without down-sampling image size and discarding image information. The size constraints on the input and output images can also be relaxed, and the quality of demosaicked images is improved. Experiment results show that the proposed network can improve the running speed by 42% compared with the fastest CNN-based method and achieve comparable reconstruction quality as it on four mainstream datasets. Besides, when we carry out the inference processing on the demosaicked images on typical deep CNN networks, Mobilenet v1 and SSD, the accuracy can also achieve 85.83% (top 5) and 75.44% (mAP), which performs comparably to the existing methods. The proposed network has the highest computing efficiency and lowest parameter number through all methods, demonstrating that it is well suitable for applications on modern edge computing devices. In an ideal color digital camera system, there should be three image sensors that capture the red, green, and blue light component signals of photo images, respectively. However, that is complex and expensive. Now, most color digital cameras normally use an image sensor with a top color filter array (CFA) to capture the intensity of a single color component signal per pixel. There are different types of CFA patterns depending on the manufacturer, such as Bayer CFA, Yamanaka CFA, and HVS-based CFA . Among tTraditional image demosaicking methods include interpolation-based methods, dictionary-based methods, etc. Malvar  and WangDue to the rapid development of deep learning in image processing, demosaicking methods based on CNN have also been proposed in recent years ,12,13,14To speed up the CNN-based image demosaicking process, this paper proposes a compact demosaicking neural network based on UNet++. It inserts densely-connected layer blocks and adopts Gaussian smoothing layers instead of pooling layers before the backbone network to reduce the model parameters and extract mosaic image features efficiently. Furthermore, it also improves the quality of demosaicked images, and the size constraints on the input and output images are relaxed, well suitable for applications on modern edge computing devices.In recent years, the rapid development of hardware architectures has opened up the implementation of deep learning CNNs on modern AI image processors or other edge computing devices. More and more deep learning methods are being used in image processing. Due to the similarity between demosaicking and image super-resolution tasks, some CNN-based demosaicking methods have been gradually proposed. Inspired by SRCNN  and VDSRInspired by the fact that the Bayer pattern contains twice as many green pixels as the sum of red and blue ones, Tan  proposedKokkinos  proposedCui  exploredUNet++  is an imSince UNet++ is an encoder–decoder network, it is suitable for image restoration tasks such as image denoising and defogging as well . The U-NMSE) of the pairs of the estimated image MSE loss. In this paper, the training process’s loss function is divided into two parts.The image-demosaicking problem is an optimization problem. It can be given as :(1)Iraw=vertible . In contSSIM as the loss function for the first ten epochs during training, which allows the network to learn the structural information of the image quickly:SSIM values of the corresponding pixel locations of two images. SSIM [SSIM calculation process, a window of size n is first specified to slide over the corresponding pixels of the two images. As shown in the above equation, SSIM, the stronger the structural similarity between the two images. Then, since the optimization process of the network is the minimization of the loss function, the loss function for SSIM can be then written as [SSIM values of each pixel. The sliding window size for calculating SSIM is taken as 11 in our proposed network’s training process in this paper.According to , we use es. SSIM  is a metitten as :(5)ℒSSIMMSE loss so that the network can subsequently learn the image details slowly, as shown in the following equation:After ten epochs, we change the loss function to L1, L2, and L3 outputs needs to be considered together as the final loss function during training. Therefore, the final loss function of our network training is expressed as:Since our network is deeply supervised and contains three levels of outputs, the loss of That is our overall optimization objective function.Our proposed network consists of two parts: image feature extraction and image reconstruction, which are shown in The overall network structure is based on a deeply supervised UNet++ structure. The advantage is that when training in this deeply supervised manner, we can get outputs from different levels and can choose a sub-network from the whole model as a trade-off between the demosaicking computational costs and accuracy. Besides, the network uses Gaussian smoothing layers instead of pooling layers to expand the receptive field of the input mosaic image and keep the image size unchanged. Furthermore, it also inserts a densely-connected layer unit adopting depthwise separable convolutions at the beginning of each level to fully extract features of the mosaic image with a small number of model parameters.L1, L2, and L3.Before entering the network, the input mosaic image is arranged into a four-channel pattern of RGGB and blurred separately through a Gaussian kernel for the number of times according to the level of the network at the corresponding layer. For example, in our proposed structure, the deepest level is 3, so the image will be blurred one, two, and three times and then enter the feature extraction part separately. After that, feature maps at each level will be concatenated with those from the former and lower layers and then go through the reconstruction node level by level until the top. At last, they will get out as three outputs: Both U-Net and UNet++ use pooling to down-sample images to obtain image features at different scales. However, for different layer levels in U-Net++, it cannot be guaranteed that an image of arbitrary size can be reconstructed to the same size as the original after several down-sampling and up-sampling operations in the image-demosaicking tasks. Therefore, in our method, we use Gaussian smoothing to process the input image with a kernel size of 5 × 5. Then we obtain a series of Gaussian pyramid-like images with the same image size and the same number of channels as the input mosaic image after each level, but with different degrees of blur. Therefore, the original down-sampling operation in the UNet++ backbone is replaced by a Gaussian smoothing layer. The advantage is that it does not change the image size, so the output and input image sizes of the network are unified while making it possible for the network to process input images of arbitrary size. More importantly, it also expands the receptive field of each input layer to fully extract features without discarding image information, unlike pooling . The yelTo ensure adequate extraction of image features with a sufficiently small number of network parameters, we exploited the characteristics of networks with a densely connected  structurnf and the number of intermediate channels gc. The values of the parameters nf and gc used in our training process at each level are given in Noting that the Dense Unit is characterized by the same number of input channels as the number of output channels. Other optional parameters are the number of input channels After the feature extraction part, the features of each level are then concatenated with the results of the former and lower layers. It will go rightward and upward until the top and get out hierarchically. That forms the image construction part of the network.The reconstruction part is still densely cross-connected, like UNet++. The blue node in The input image size and the size of the feature map inside the network at each level keep the same until final up-sampling, after which it is two times the original height and width. The details of feature map size and kernel size at each reconstruction node during the network are shown in During the training process, we add all three levels of outputs to the loss function to provide feedback to the network, and the sub-network of different layers can be taken out separately during the inference process, making it flexible in choosing a different scale of the network to suit different application needs. The higher the level of the network output, the higher the demosaicking accuracy, and the larger the number of parameters.With a small number of network parameters, the selection of a suitable training set becomes more important for the training process. To have a better reconstruction performance on various kinds of scenes, we choose ImageNet , which cm. The output o of the network is of the same size as the original ground-truth RGB patch, which is 128 × 128 × 3.We use one of the Bayer pattern GBRG as the filter to produce mosaic patches. As shown in As for training settings, we adopt Adam  as the oPSNR) value, or the color peak signal-to-noise ratio (CPSNR) value as the metric to measure the reconstruction accuracy of the demosaicked image, as defined below [For the demosaicking task, there are two most widely used datasets, Kodak  and McMaed below :(8)CPSNRPSNRs (for each single color) and the CPSNRs (for the whole image of three colors). Due to their difference in padding and other pre-processing strategies, a 5-pixel at each edge was ignored when testing the PSNR values. The results are shown in Each algorithm was tested on four datasets to get the PSNR metric. Although the proposed method does not reach the highest PSNR value, it is among top-level, indicating that it can reconstruct sufficient high-quality images. Meanwhile, we selected several images with some representative as well as difficult structures from those datasets to give the visual reconstruction comparison in From the table, we can see that the performance of the CNN-based methods is substantially better than that of the traditional ones in terms of the PSNRs in PSNR value of our method is not the highest, it still shows an excellent performance in highly difficult demosaicking scenes such as stripes without generating artifacts like zippering and false color.From the visual comparison of demosaicking results above, we can see that AHD  has a bePSNR while ignoring the computational cost loses its meaning for practical application. As mentioned in [Image-demosaicking is an important part of the modern camera ISP pipeline. The reconstruction accuracy of the demosaicking method is important, but blindly pursuing high ioned in , currentWe measured the running time required on a total of 18 images of the shape 500 × 500 in McMaster datasets  on the NDue to the differences in the generation and pre-processing between each method for the input mosaic images, the time we measured above includes the process from the original RGB image to the generation of mosaic images. Each method was tested five times and averaged as the final result. Among them, the traditional method, AHD , DLMMSE As for Tan , KokkinoPSNRs and running time is illustrated by the scatter in As shown in PSNR and lower time cost, whereas the proposed method (L3) outperforms five out of the eight methods by both PSNR and running time. Compared with Kokkinos [PSNR, the proposed method only loses 2.37% and 2.43% of the PSNR value while improving the running time by 72.18% and 79.59%. Compared with the fastest method in CNN-based methods Tan [We can see from the plot that none of the methods above outperforms our proposed method by both higher Kokkinos  and Cui Kokkinos , which ahods Tan , the proConsidering the wide application of edge computing devices for image classification and detection tasks, we performed each demosaicking method before the image classification and detection tasks, simulating the process of outputting mosaic images from image sensors, going through demosaicking algorithms, and then passing through classification or detection networks. We tested the accuracy of classification and detection tasks after different demosaicking algorithms and compared the reconstruction effect. We selected MobileNet v1  and SSD To the image classification task on MobileNet v1, for each original RGB image in the test set, the corresponding reconstructed image was generated first according to the demosaicking method mentioned in To the target detection task, we first took the same operation for each input RGB image in the VOC2007 test set as the image classification part to generate the reconstruction images. After that, each image was resized to 300 × 300 and then entered SSD for detection. Finally, the average detection accuracy  over thWe list the accuracy values of each demosaicking method after connected with the image classification and detection tasks in As shown in In this section, we add an ablation study for the Gaussian smoothing layers and discuss its impact in the proposed network. In this study, we propose a compact, high-efficiency end-to-end demosaicking convolutional neural network for the current application needs on edge computing devices. By adding densely connected layer blocks and using depthwise separable convolutions, we made full use of the correlations between the features of the images themselves for computation, which greatly reduced the number of parameters of the network but still achieved excellent performance. Besides, we used Gaussian smoothing instead of down-sampling input images to expand the receptive field and to relax the constraints on the size of input images without discard any image information. Furthermore, since Gaussian smoothing can play a certain degree of denoising, and its use leads the network to be able to extract multi-scale image features, the proposed network has the potential to perform well in other image restoration tasks such as denoising and super-resolution. We will apply the proposed network structure to these tasks in future research.PSNR). Besides, the result of inference processing on the demosaicked images on Mobilenet v1 and SSD indicates that the accuracy can also achieve a high level that is performed comparably to the existing methods. Moreover, it contains only several convolutional computations with high parallelism and can handle images of arbitrary size without special pre-processing operations for the input mosaic images, making it easily connected with image sensors with a CFA pattern. Its deeply supervised training manner makes it flexible to be pruned during inference. As a result, the proposed methods can be efficiently applied to some edge computing devices such as AI accelerators and has the potential for efficient processing on edge devices that support parallel processing such as Application Specific Integrated Circuit (ASIC) or intelligent camera processors for high-quality image demosaicking.The above experiment results demonstrated that our proposed method achieves the leading demosaicking accuracy in terms of both subjective visual comparisons and the objective metric (