A unified objective interpolating between classical and generative phase retrieval gives error bounds and appears to lower reconstruction error across noise levels in a small MNIST experiment.
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PtyGenography: using generative models for regularization of the phase retrieval problem
A unified objective interpolating between classical and generative phase retrieval gives error bounds and appears to lower reconstruction error across noise levels in a small MNIST experiment.