Ptychographic phase-retrieval by proximal algorithms

TL;DR

This paper introduces new ptychographic phase-retrieval algorithms based on proximal methods, improving noise robustness and convergence speed, with experimental validation demonstrating superior performance of the accelerated proximal gradient approach.

Contribution

It develops a unified proximal algorithm framework for ptychography, incorporating noise effects via maximum-likelihood, and benchmarks three algorithms showing accelerated proximal gradient's advantages.

Findings

Accelerated proximal gradient outperforms other algorithms in accuracy.

Proximal algorithms effectively incorporate noise modeling.

Numerical and experimental results validate the proposed methods.

Abstract

We derive a set of ptychography phase-retrieval iterative engines based on proximal algorithms originally developed in convex optimization theory, and discuss their connections with existing ones. The use of proximal operator creates a simple frame work that allows us to incorporate the effect of noise from a maximum-likelihood principle. We focus on three particular algorithms, namely proximal minimization, alternating direction method of multiplier and accelerated proximal gradient, and benckmark their performance with numerical simulations and experimental x-ray data. Among them, accelerated proximal gradient shows superior performance in terms of both accuracy and convergence rate for a noisy dataset.