Phase retrieval with physics informed zero-shot network.

Abstract

Phase can be reliably estimated from a single diffracted intensity image if faithful prior information about the object is available. Examples include amplitude bounds, object support, sparsity in the spatial or transform domain, deep image prior, and the prior learned from labeled datasets by a deep neural network. Deep learning facilitates state-of-the-art reconstruction quality but requires a large labeled dataset (ground truth measurement pair acquired in the same experimental conditions) for training. To alleviate this data requirement problem, this Letter proposes a zero-shot learning method. The Letter demonstrates that the object prior learned by a deep neural network while being trained for a denoising task can also be utilized for phase retrieval if the diffraction physics is effectively enforced on the network output. The Letter additionally demonstrates that the incorporation of total variation in the proposed zero-shot framework facilitates reconstruction of similar quality in less time (e.g., ∼9 fold, for a test reported in this Letter).