PR-DAD: Phase Retrieval Using Deep Auto-Decoders

TL;DR

PR-DAD introduces a novel deep auto-decoder architecture for phase retrieval, leveraging mathematical modeling to outperform existing deep learning and classical methods in recovering images from Fourier magnitude data.

Contribution

The paper presents a new deep auto-decoder architecture for phase retrieval, integrating mathematical modeling to enhance performance beyond current approaches.

Findings

PR-DAD surpasses all existing methods in phase retrieval accuracy.

Experimental results demonstrate significant improvements over classical algorithms.

The architecture effectively models the phase retrieval problem, leading to better image reconstruction.

Abstract

Phase retrieval is a well known ill-posed inverse problem where one tries to recover images given only the magnitude values of their Fourier transform as input. In recent years, new algorithms based on deep learning have been proposed, providing breakthrough results that surpass the results of the classical methods. In this work we provide a novel deep learning architecture PR-DAD (Phase Retrieval Using Deep Auto- Decoders), whose components are carefully designed based on mathematical modeling of the phase retrieval problem. The architecture provides experimental results that surpass all current results.