# Retina-like Imaging and Its Applications: A Brief Review
## 2 Principle and Methods
### Methods
Although the passive methods effectively select and receive target information, the efficiency can be further improved if the required information can be extracted directly. Therefore, the active methods of retina-like imaging have been proposed. In 2017, adaptive, foveated, single-pixel imaging was proposed by [PERSON] et al. [31]. Given that traditional single-pixel imaging requires a large number of measurements for image reconstruction, the target of interest is reconstructed in high-definition with the help of the human eye gaze function, which improves the reconstruction efficiency. Our group combined the retina-like structure with light detection and ranging (LiDAR). The space-variant scanning based on a micro-electro-mechanical system (MEMS) [17] and an optical phased array [32] were proposed. Compared with traditional LiDAR imaging, this retina-like structure maintains the high resolution in the fovea while enlarging the FOV. Importantly, these methods are simply an improvement on the scanning strategy and need no additional devices. For software-based method, the algorithm model construction inspired by the human retina has attracted the attention of many researchers. In 2008, [PERSON] et al. [33] proposed a 3D reconstruction method based on the human retina, and it relies on the rotation and scaling invariance of log-polar transform to rebuild the view of room. In the same year,Wong et al. [34] used log-polar transform to realize panoramic imaging with high efficiency based on FPGA. It decomposes the 256 \(\times\) 256 omnidirectional image into a 128 \(\times\) 128 panoramic image in the logarithmic polar coordinate. In 2016, [PERSON] et al. [35] used the visual attention mechanism of the human eye to solve the problem of high-precision recognition of small targets. Compared with the fixed sampling model, this method reduces the error rate of recognition by half. Figure 3 shows the summary of retina-like imaging methods. The software methods are the least difficult to implement and the most tightly integrated with the other features, such as attention mechanism or omnidirectional imaging. However, more computation cost is required because this method is a further processing of the redundant information. Meanwhile, the information has been screened in the imaging process of the hardware methods, including passive and active methods. In this way, imaging has low redundancy while having increased technical difficulty. Through vertical comparison, more functional retina-like imaging becomes the central issue of bionic imaging. Especially after 2017, fovea gaze control, lens-like longitudinal focus adjustment, and multi-center imaging are combined in the retina-like imaging and achieve good effect. The comparison of these methods is shown in Table 1. Passive methods, which require more improvement on the imaging devices, have the highest difficulty. Meanwhile, active methods also compress the data volume, and the traditional devices are still suitable for retina-like imaging. Therefore, the difficulty in the space-variant scanning strategy is the requirement of more cooperation between imaging elements. The software methods, which are a processing of the imaging, have simple structure and can be flexibly used in different situations. The disadvantage of poor real-time performance will be alleviated with the improvement in computing power. Figure 3: Implementation methods of retina-like imaging.