Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial-Reference-Cited by-同舟云学术

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

Published:2023-01 Issue:1 Volume:40 Page:45-57
ISSN:1053-5888
Container-title:IEEE Signal Processing Magazine
language:
Short-container-title:IEEE Signal Process. Mag.

Author:

Dong Jonathan¹^ORCID,Valzania Lorenzo²^ORCID,Maillard Antoine³^ORCID,Pham Thanh-an⁴^ORCID,Gigan Sylvain²^ORCID,Unser Michael¹^ORCID

Affiliation:

1. Biomedical Imaging Group, École polytechnique fédérale de Lausanne, Lausanne, Switzerland

2. Laboratoire Kastler Brossel, École Normale Supérieure–Paris Sciences et Lettres Research University, Sorbonne Université, Centre National de la Recherche Scientifique Unité Mixte de Recherche 8552, Collège de France, Paris, France

3. Department of Mathematics and the Institute for Mathematical Research, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland

4. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Funder

European Research Council

Swiss National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/79/10004746/10004797.pdf?arnumber=10004797

Reference50 articles.

1. Saving phase: Injectivity and stability for phase retrieval

2. Optimal Spectral Initialization for Signal Recovery With Applications to Phase Retrieval

3. Fundamental Limits of Weak Recovery with Applications to Phase Retrieval

4. The high-dimensional asymptotics of first order methods with random data;celentano,2021

5. Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

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2. The performance of the amplitude-based model for complex phase retrieval;Information and Inference: A Journal of the IMA;2024-01-01

3. On the use of deep learning for phase recovery;Light: Science & Applications;2024-01-01

4. Phase Retrieval Based on Supervised Machine Learning Regression Algorithms;2023 Workshop on Communication Networks and Power Systems (WCNPS);2023-11-30

5. Res-U2Net: Augmenting 2D/3D Image Reconstruction through Untrained Deep Learning Models for Phase Retrieval Enhancement;2023 20th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE);2023-10-25