Optimization-Based Image Restoration under Implementation Constraints in Optical Analog Circuits

TL;DR

This paper explores implementing image restoration algorithms with total variation regularization on optical analog circuits, addressing challenges like division operations and optical noise, and demonstrating effective denoising through simulations.

Contribution

It designs optical circuit structures for ADMM and PDS algorithms that avoid division operations and account for optical amplifier noise, enabling practical image restoration.

Findings

Effective denoising achieved in simulations

Circuit design avoids division with dynamic variables

Optical noise impact incorporated in the model

Abstract

Optical analog circuits have attracted attention as promising alternatives to traditional electronic circuits for signal processing tasks due to their potential for low-latency and low-power computations. However, implementing iterative algorithms on such circuits presents challenges, particularly due to the difficulty of performing division operations involving dynamically changing variables and the additive noise introduced by optical amplifiers. In this study, we investigate the feasibility of implementing image restoration algorithms using total variation regularization on optical analog circuits. Specifically, we design the circuit structures for the image restoration with widely used alternating direction method of multipliers (ADMM) and primal dual splitting (PDS). Our design avoids division operations involving dynamic variables and incorporate the impact of additive noise introduced by optical amplifiers. Simulation results show that the effective denoising can be achieved in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) even when the circuit noise at the amplifiers is taken into account.