# Retina-like Imaging and Its Applications: A Brief Review
## 3 Applications
### Ghost Imaging  
Ghost imaging (GI), which is a novel imaging technology, has attracted much attention due to its advantages, such as low cost, wide spectrum range, and robustness to light scattering [66, 67]. GI provides scene information by correlating the modulate light patterns generated by the pseudo-thermal source, digital micromirror device (DMD), or other types of spatial light modulators [68] with the intensity of the light from the target scene. However, GI using traditional light modulation patterns, such as random ones, cannot balance imaging efficiency and quality [4, 69]. Some researchers have conducted studies on the retina-like patterns of GI as inspired by the human eye to improve the performances of GI. A strategy that exploits the spatiotemporal redundancy was presented by [PERSON] et al. [31]. This strategy rapidly records the detail information of quickly moving features in the scene and accumulates details of slower evolving areas over several consecutive frames. The retina-like patterns are created by reformatting each row of the Hadamard matrix into a 2D grid of spatially variant pixel size, as shown in Figure 14D,E. Figure 14C,F show the imaging results of traditional patterns and retina-like patterns, and using a retina-like structure, enhances the detail in the foveal region at the expense of a lower resolution in the periphery. They also realized the dynamic imaging of single fovea area and dual fovea through experiments. This proposed method is a novel approach for compressive sensing and improves the performances of GI. A model of 3D GI combined with retina-like structure (R-3 DCGI) is described for improving imaging efficiency by our group, as shown in Figure 15 [70]. A signal generator (SG) triggers a pulsed laser (PL). Random speckles are produced by the DMD that are illuminated by the PL, which is used to illuminate a target. The one-dimensional time-resolved total intensity distribution of a reflected or scattered light from the target is collected by a receiving lens (RL) and a time-resolved bucket detector (TBD). The 3D  
Figure 14: GI with retina-like patterns. (**A**) Uniform grid with 1024 pixels. (**B**) Examples of a complete 1024 pattern. (**C**) The reconstructed image with uniform grid. (**D**) Spatially variant pixel grid with 1024 pixels. (**E**) Examples of a complete 1024 pattern with spatially variant grid. (**F**) The reconstructed image with spatially variant grid. Figure 13: Architecture of polar transformer network. imaging is obtained by combining images from different depth slices. However, the retina-like structure of R-3 DCGI is based on logarithmic polar coordinates, as shown in Figure 16. Some retina-like properties, such as invariant scaling and rotation, have been realized in R-3 DCGI. The other advantage is a higher imaging efficiency than traditional 3D GI for using a retina-like structure. A foveal GI based on deep learning to realize the intelligent selection of the ROI for foveal imaging was proposed by [PERSON] et al. [71]. Selecting the ROI intelligently by applying generative adversarial networks based on SSD architecture improves the imaging quality with higher PSNR of ROI than that of uniform-resolution GI can be achieved. [PERSON] et al. [72] proposed a novel compressive GI called R-CSGI, as inspired by human consciousness controlling the eyes to acquire the ROI. This method uses prior imaging information from fast Fourier single-pixel imaging to achieve a better visual effect and higher imaging quality. The principle of R-CSGI is shown in Figure 17. A sequence of fast Fourier basis patterns is illuminated on the object, and the image is reconstructed by the light intensity collected by a single-pixel detector. The ROI is then generated by the reconstructed image. The real-value random patterns used to perform compressive sensing reconstruction are generated according to the ROI. The advantage of this method is that the imaging quality obtained by compressive sensing technique can be effectively enhanced because the ROI is found previously.