Improved success rate and stability for phase retrieval by including randomized overrelaxation in the hybrid input output algorithm

TL;DR

This paper introduces a randomized overrelaxation extension to the hybrid input-output algorithm, significantly improving the success rate and stability of phase retrieval reconstructions without increasing computational costs.

Contribution

The paper presents a novel extension of the hybrid input-output algorithm using randomized overrelaxation, enhancing success rates in phase retrieval tasks.

Findings

Enhanced success rate of phase retrieval reconstructions

Extension outperforms traditional algorithms in most cases

No additional computational cost or memory increase

Abstract

In this paper, we study the success rate of the reconstruction of objects of finite extent given the magnitude of its Fourier transform and its geometrical shape. We demonstrate that the commonly used combination of the hybrid input output and error reduction algorithm is significantly outperformed by an extension of this algorithm based on randomized overrelaxation. In most cases, this extension tremendously enhances the success rate of reconstructions for a fixed number of iterations as compared to reconstructions solely based on the traditional algorithm. The good scaling properties in terms of computational time and memory requirements of the original algorithm are not influenced by this extension.