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., ${\sim}9$ fold, for a test reported in this Letter).

中文翻译：

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使用物理信息的零样本网络进行相位检索

如果可以获得关于对象的可靠先验信息，则可以从单个衍射强度图像中可靠地估计相位。示例包括幅度边界、对象支持、空间或变换域中的稀疏性、深度图像先验以及通过深度神经网络从标记数据集中学习的先验。深度学习促进了最先进的重建质量，但需要大量标记数据集（在相同实验条件下获得的地面实况测量对）进行训练。为了缓解这种数据需求问题，本文提出了一种零样本学习方法。这封信表明，如果衍射物理在网络输出上得到有效执行，则在接受去噪任务训练时由深度神经网络先验学习的对象也可用于相位检索。${\sim}9$折，用于本信函中报告的测试）。