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On the interplay between physical and content priors in deep learning for computational imaging
That is, the higher the entropy of the training images, the weaker the regularization effect can be imposed.
Learning to Synthesize: Robust Phase Retrieval at Low Photon counts
The quality of inverse problem solutions obtained through deep learning [Barbastathis et al, 2019] is limited by the nature of the priors learned from examples presented during the training phase.
Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery
Deep Neural Network (DNN)-based image reconstruction, despite many successes, often exhibits uneven fidelity between high and low spatial frequency bands.
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