Phase-Retrieval as a Regularization Problem

TL;DR

This paper links phase-retrieval imaging to optimization methods, enabling the numerical estimation of physical parameters that are otherwise difficult to determine, thereby improving image reconstruction accuracy.

Contribution

It introduces a novel approach connecting phase-retrieval algorithms with optimization strategies to estimate physical parameters automatically.

Findings

The proposed method accurately estimates physical parameters in phase retrieval.

Numerical experiments demonstrate improved image reconstruction quality.

The approach simplifies the phase retrieval process by integrating parameter estimation.

Abstract

It was recently shown that the phase retrieval imaging of a sample can be modeled as a simple convolution process. Sometimes, such a convolution depends on physical parameters of the sample which are difficult to estimate a priori. In this case, a blind choice for those parameters usually lead to wrong results, e.g., in posterior image segmentation processing. In this manuscript, we propose a simple connection between phase-retrieval algorithms and optimization strategies, which lead us to ways of numerically determining the physical parameters