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Phase retrieval with physics informed zero-shot learning
[article]
2021
arXiv
pre-print
Phase can be reliably estimated from a single diffracted intensity image, if a faithful prior information about the object is available. Examples include amplitude bounds, object support, sparsity in the spatial or a transform domain, deep image prior and the prior learnt from the labelled datasets by a deep neural network. Deep learning facilitates state of art reconstruction quality but requires a large labelled dataset (ground truth-measurement pair acquired in the same experimental
arXiv:2106.04577v1
fatcat:o7j5pypztvdzjbmzydegpv6odm