Unfolding-Aided Bootstrapped Phase Retrieval in Optical Imaging

TL;DR

This paper reviews deep unfolding algorithms for phase retrieval in optical imaging, highlighting their ability to improve DOE design and handle complex scenarios across various imaging zones.

Contribution

It provides an overview of deep unfolding methods applied to bootstrapped phase retrieval, emphasizing their advantages over traditional techniques.

Findings

Deep unfolding enhances phase retrieval accuracy.

Hybrid approaches improve DOE design under theoretical conditions.

Applicable across near, middle, and far imaging zones.

Abstract

Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless data acquired in the form of its diffraction patterns. These patterns are acquired through a system with a coherent light source that employs a diffractive optical element (DOE) to modulate the scene resulting in coded diffraction patterns at the sensor. Recently, the hybrid approach of model-driven network or deep unfolding has emerged as an effective alternative to conventional model-based and learning-based phase retrieval techniques because it allows for bounding the complexity of algorithms while also retaining their efficacy. Additionally, such hybrid approaches have shown promise in improving the design of DOEs that follow theoretical uniqueness conditions. There are opportunities to exploit novel experimental setups and resolve even more complex DOE phase retrieval applications. This paper presents an overview of algorithms and applications of deep unfolding for bootstrapped - regardless of near, middle, and far zones - phase retrieval.