Self-supervised model-driven deep learning for two-step phase-shifting interferometry.

Data-driven deep learning methods are widely applied in interferometry. However, their performance depends heavily on the quality of the training datasets, which limits both accuracy and generalization. This Letter introduces a model-driven deep-lear…

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