Robust Phase Retrieval with Green Noise Binary Masks

TL;DR

This paper introduces a green noise binary masking scheme for phase retrieval that reduces high frequency content in masks, improving reconstruction performance in DMD-based systems by combining randomness with non-bandlimited properties.

Contribution

It proposes a novel green noise binary mask design inspired by digital halftoning, addressing high frequency issues in traditional random masks for phase retrieval.

Findings

Green noise masks outperform traditional masks in phase retrieval accuracy.

The proposed masks reduce high frequency content while maintaining randomness.

Experimental results confirm improved performance in DMD-based systems.

Abstract

Phase retrieval with pre-defined optical masks can provide extra constraint and thus achieve improved performance. The recent progress in optimization theory demonstrates the superiority of random masks in phase retrieval algorithms. However, traditional approaches just focus on the randomness of the masks but ignore their non-bandlimited nature. When using these masks in the reconstruction process for phase retrieval, the high frequency part of the masks is often removed in the process and thus leads to degraded performance. Based on the concept of digital halftoning, this paper proposes a green noise binary masking scheme which can greatly reduce the high frequency content of the masks while fulfilling the randomness requirement. The experimental results show that the proposed green noise binary masking scheme outperform the traditional ones when using in a DMD-based coded diffraction pattern phase retrieval system.