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

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Phase retrieval from a single diffraction intensity pattern by generating the support constraint using deep learning;Optics & Laser Technology;2025-02

2. Roadmap on computational methods in optical imaging and holography [invited];Applied Physics B;2024-08-29

3. Phase retrieval based on the distributed conditional generative adversarial network;Journal of the Optical Society of America A;2024-08-08

4. Deep learning-based general beam synthesis for atmospheric propagation;Optics Express;2024-07-29

5. Advanced Optical Imaging Technologies for Microplastics Identification: Progress and Challenges;Advanced Photonics Research;2024-07-22