Restoration of motion-blurred numeral image using a complex-amplitude diffractive processor

Abstract

We propose a complex-amplitude diffractive processor based on diffractive deep neural networks (D2NNs). By precisely controlling the propagation of an optical field, it can effectively remove the motion blur in numeral images and realize the restoration. Comparative analysis of phase-only, amplitude-only, and complex-amplitude diffractive processor reveals that the complex-amplitude network significantly enhances the performance of the processor and improves the peak signal-to-noise ratio (PSNR) of the images. Appropriate use of complex-amplitude networks contributes to reduce the number of network layers and alleviates alignment difficulties. Due to its fast processing speed and low power consumption, complex-amplitude diffractive processors hold potential applications in various fields including road monitoring, sports photography, satellite imaging, and medical diagnostics.