PR-DAD: Phase Retrieval Using Deep Auto-Decoders
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 18 Apr 2022.
Builds on
8 of 20 references.
References · 8
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
- 2014Deep Learning Face Attributes in the Wildcited by 3,693
- 2018Deep Learning for Classical Japanese Literaturecited by 422
- 2018prDeep: Robust Phase Retrieval with a Flexible Deep Networkcited by 47
- 2019Online Regularization by Denoising with Applications to Phase Retrievalcited by 12
- 2019Phase Retrieval Using Conditional Generative Adversarial Networkscited by 11
- 2020DeepPhaseCut: Deep Relaxation in Phase for Unsupervised Fourier Phase Retrievalcited by 8
- 2021Non-Iterative Phase Retrieval With Cascaded Neural Networkscited by 6
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Citing papers per year: 2025: 1.
1 paper.