Recently, single-shot phase retrieval techniques, which aim to reconstruct an original sample from a single near-field diffraction pattern, have garnered significant attention. Despite their promise, existing methods are highly dependent on precise physical forward models, constraining their effectiveness in real-world scenarios. To overcome the challenges posed by unknown diffraction distances in blind single-shot phase retrieval, this paper introduces a self-supervised physics-adaptive neural network termed BlindPR-SSPANN. The proposed method jointly optimizes the physical parameters of the forward propagation model alongside the trainable parameters of the reconstruction network. To achieve this, BlindPR-SSPANN incorporates a novel network architecture that integrates tunable physical parameters within a multi-stage, coupled reconstruction process. The proposed network is trained under a self-supervised scheme facilitated by a refined physics-consistent loss function. Simulation and experimental results demonstrate that BlindPR-SSPANN delivers high-performance reconstructions from a single intensity measurement, even under large diffraction distance errors, enabling self-calibrated snapshot coherent diffraction imaging.

最近，单次拍摄相位恢复技术引起了广泛关注。这些技术旨在从单一的近场衍射图案中重建原始样本。尽管它们很有前景，但现有的方法高度依赖于精确的物理前向模型，在实际场景中的有效性受到了限制。为了克服盲单次拍摄相位恢复中未知衍射距离带来的挑战，本文提出了一种自监督的物理适应性神经网络，称为BlindPR-SSPANN。该方法同时优化了前向传播模型的物理参数以及重建网络的可训练参数。为此，BlindPR-SSPANN引入了一种新的网络架构，将可调物理参数整合到一个多阶段耦合重建过程中。该网络在自监督方案下进行训练，并通过一个精炼的物理一致损失函数来实现。仿真和实验结果表明，即使在较大的衍射距离误差情况下，BlindPR-SSPANN也能从单一强度测量中获得高性能的重建图像，从而实现了自校准快照相干衍射成像。