This paper puts forward a retina-like sensor based on a lens array, which can be used in conventional optical systems. This sensor achieves log-polar mapping by dividing the imaging optical system's image plane using a lens array. In this paper the mathematical model has been set up with the relative structural parameters. Also, the simulation experiments and parameter analysis have been discussed to verify the reliability of this system. From the experiment results, it can be seen that this sensor realized the log-polar mapping with the transformed image output. Each lens corresponded to a circular region in the image plane with no crossover between different fields of view of adjacent lenses. When the number of rings changed, the relative error did not significantly change, and this error could be reduced to 1% when the number of lenses in each ring was increased. The work widely enlarged the application of this kind of sensor, which will lay a theoretical foundation for retina-like sensors.

This paper presents an optical stabilization system based on deformable mirrors (DMs) for retina-like sensors. This system achieves image stabilization by changing the reflective plate of the DM's compensating tilt angle. The mathematical model is constructed with relative parameters, and the simulation experiments and parameter analysis are discussed to verify the system's reliability. The experimental results show that this system achieved optical image stabilization. The maximum relative error of the compensation angle is 8.78%. The system is close to the diffraction limit, and the distortion is less than 0.33%. This study presents an image stabilization system and offers possible improvement in the aberrations in the system, which will provide great support to retina-like sensors.

jections was assessed by two metrics, including the peak signal-to-noise ratio (PSNR) and the contrast-to-noise ratio (CNR). The results show the accuracy of the cortical segmentation was 96.07%. The PSNR and CNR values increased significantly in the projections of the selected cortical regions. The OCTA incorporating the deep learning-based cortical segmentation can efficiently improve the image quality and enhance the vasculature clarity.

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