Opt Lett. 2023 May 15;48(10):2732-2735. doi: 10.1364/OL.487150.
Learning-based phase imaging balances high fidelity and speed. However, supervised training requires unmistakable and large-scale datasets, which are often hard or impossible to obtain. Here, we propose an architecture for real-time phase imaging based on physics-enhanced network and equivariance (PEPI). The measurement consistency and equivariant consistency of physical diffraction images are used to optimize the network parameters and invert the process from a single diffraction pattern. In addition, we propose a regularization method based total variation kernel (TV-K) function constraint to output more texture details and high-frequency information. The results show that PEPI can produce the object phase quickly and accurately, and the proposed learning strategy performs closely to the fully supervised method in the evaluation function. Moreover, the PEPI solution can handle high-frequency details better than the fully supervised method. The reconstruction results validate the robustness and generalization ability of the proposed method. Specially, our results show that PEPI leads to considerable performance improvement on the imaging inverse problem, thereby paving the way for high-precision unsupervised phase imaging.
基于学习的相位成像兼顾高保真度和速度。然而，监督式训练需要明确且大规模的数据集，而这些数据集往往难以获取或无法获取。在这里，我们提出了一种基于物理增强网络和等变性（PEPI）的实时相位成像架构。物理衍射图像的测量一致性和等变性一致性被用来优化网络参数，并从单个衍射模式中反转过程。此外，我们提出了一种基于全变差核（TV-K）函数约束的正则化方法，以输出更多的纹理细节和高频信息。结果表明，PEPI 可以快速准确地生成物体相位，所提出的学习策略在评估函数上与完全监督方法非常接近。此外，PEPI 解可以比完全监督方法更好地处理高频细节。重建结果验证了该方法的稳健性和泛化能力。特别地，我们的结果表明，PEPI 可以显著提高成像反问题的性能，从而为高精度的无监督相位成像铺平了道路。
