# Retina-like Imaging and Its Applications: A Brief Review
## 4 Discussion  
We learn the methods of retina-like imaging, including hardware and software, from Section 2. Meanwhile, the view of practical use in Section 3 indicates that various applications use the properties of retina-like imaging. Although the theory of retina-like imaging is relatively good, some challenges exist in methods and applications. Here, we select several typical challenges for discussion. First is the challenge of the optimization of multi parameters of retina-like imaging. According to Equation (2), the parameters of retina-like imaging include rings (\(M\)), sectors (\(N\)), the radius of the blind hole (\(r_{0}\)), and the maximum radius of the outmost ring (\(r_{max}\)). These parameters are correlated and designed by practical use. For example, from the view of electro-based method, fill factor (FF) should be given more consideration due to achieving high optical efficiency. Meanwhile, the pixel crosstalk noise should be considered due to the space-variant size of photosurfaces, leading to low SNR. A space-variant lens array has been proposed to increase the distance between the neighbor pixels [23] for solving the above-mentioned issues. As a result, the size of  
Figure 17: Principle of the R-CSGI.  
Figure 18: Measurement principle of the parallel retina-like computational GI.  
each pixel is the same and is beneficial of fabricating. However, the optical aperture is supposed to be circle, and the maximum of FF based on space-variant lens is \(\pi/4\) due to the physical limitations of lens. Therefore, other structures of lens array have been proposed to increase FF [75]. Therefore, optimizing or balancing the retina-like parameters is important to improve the performances for different practical uses.  
Second, improving the performance of retina-like GI is also a challenge. From the view of the GI mechanism, the method based on retina-like patterns provides a potential method for balancing the trade-off between the resolution and imaging efficiency of GI. Compared with space-invariant patterns, combining the retina-like GI is efficient for improving the imaging quality and shortening the imaging time. For example, super-resolution reconstruction methods are efficient to improve the spatial resolution limited by the hardware device (e.g., the micro-mirror size of DMD), such as sub-pixel shifting. Meanwhile, the time cost is extremely reduced when the parallel GI structure is combined. The invariant rotation and scaling, as the main features of the retina-like structure, are used on the GI system to track moving objects, especially with the object that needs to vary the size and the movement forms on the same FOV. With the retina-like patterns, the change in target need not be considered during the imaging reconstruction. The complexity of data processing is also reduced, which improves the efficiency of the GI with moving objects.  
The third challenge is deep learning combined with retina-like feature. Although the retina-like mechanism has been widely used in deep learning, it still has some limitations. For example, deep learning networks have a strong dependence on datasets, which restricts its application in many practical scenarios, such as defect detection, military target identification, and medical image processing. The mechanism of retina-like imaging mitigates the effect of redundant data. However, the way to select the information that needs to be focused on is challenging for deep learning. The application of retina-like attention mechanism to SNN is also challenging to study. SNN originated in neuroscience, and it also has an important directive value for the design of retina-like deep learning networks. Given the lightweight network parameters of SNN, it is more suitable for application in actual scenarios. The combination of retina-like architecture and SNN will be more promising.