DLMMPR:Deep Learning-based Measurement Matrix for Phase Retrieval

TL;DR

This paper introduces DLMMPR, a novel deep learning-based measurement matrix design for phase retrieval that enhances recovery accuracy and robustness through an end-to-end architecture.

Contribution

It presents the first integration of learning optimization into measurement matrix design specifically for phase retrieval, improving performance over existing methods.

Findings

Significant PSNR and SSIM improvements over benchmarks

Effective robustness across various noise levels

Validated through comprehensive empirical experiments

Abstract

This paper pioneers the integration of learning optimization into measurement matrix design for phase retrieval. We introduce the Deep Learning-based Measurement Matrix for Phase Retrieval (DLMMPR) algorithm, which parameterizes the measurement matrix within an end-to-end deep learning architecture. Synergistically augmented with subgradient descent and proximal mapping modules for robust recovery, DLMMPR's efficacy is decisively confirmed through comprehensive empirical validation across diverse noise regimes. Benchmarked against DeepMMSE and PrComplex, our method yields substantial gains in PSNR and SSIM, underscoring its superiority.