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This article reviews the various deep learning phase recovery methods from four perspectives: deep-learning-pre-processing for phase recovery, deep-learning-in-processing for phase recovery, deep-learning-post-processing for phase recovery, and deep-learning-end-to-end for phase recovery. These methods have been developed by scientists from The University of Hong Kong, Northwestern Polytechnical University, The Chinese University of Hong Kong, Guangdong University of Technology and Massachusetts Institute of Technology. These methods have their own advantages and disadvantages in terms of spatio-temporal resolution, computational complexity, and application range.
