PR-DAD: Phase Retrieval Using Deep Auto-Decoders

• URL: http://arxiv.org/abs/2204.09051v1
• Date: Mon, 18 Apr 2022 21:20:01 GMT
• Title: PR-DAD: Phase Retrieval Using Deep Auto-Decoders
• Authors: Leon Gugel and Shai Dekel
• Abstract summary: PR-DAD (Phase Retrieval Using Deep Auto- Decoders) is a novel architecture based on mathematical modeling of the phase retrieval problem. The architecture provides experimental results that surpass all current results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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