HIONet: Deep priors based deep unfolded network for phase retrieval-Reference-Cited by-同舟云学术

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HIONet: Deep priors based deep unfolded network for phase retrieval

Published:2022-12 Issue: Volume:132 Page:103797
ISSN:1051-2004
Container-title:Digital Signal Processing
language:en
Short-container-title:Digital Signal Processing

Author:

Yang Yuchi^ORCID,Lian Qiusheng,Zhang Xiaohua,Zhang Dan,Zhang Huibin

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Signal Processing,Artificial Intelligence,Applied Mathematics,Computer Vision and Pattern Recognition,Statistics, Probability and Uncertainty,Computational Theory and Mathematics

Reference61 articles.

1. Phase retrieval from coded diffraction patterns;Candes;Appl. Comput. Harmon. Anal.,2015

2. Fienup algorithm with sparsity constraints: application to frequency-domain optical-coherence tomography;Mukherjee;IEEE Trans. Signal Process.,2014

3. Phase retrieval and image reconstruction for astronomy;Fienup;Image Recovery Theory Appl.,1987

4. Alternating direction methods for classical and ptychographic phase retrieval;Wen;Inverse Probl.,2012

5. Phase retrieval in crystallography and optics;Millane;JOSA A,1990

Cited by 2 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. Real-time snapshot fractional Fourier phase retrieval via deep unfolding network;Sixth Conference on Frontiers in Optical Imaging and Technology: Novel Imaging Systems;2024-04-30

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

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