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This article reviews 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-based end-to-end phase recovery. The authors discuss the advantages and disadvantages of each method, as well as the challenges and opportunities for future research.
