Terahertz ptychography enabled by untrained physics-driven neural networks

Ptychography is a phase imaging technique that leverages intensity images obtained by translating objects across an illumination beam. Deep learning has demonstrated promising potential in solving inverse problems, offering effective solutions for phase retrieval. However, obtaining substantial amounts of labeled data in the terahertz (THz) bands for pretraining the neural networks is very challenging, thereby limiting the generalization ability of the networks. Here, we propose an untrained physics-driven neural network (UPNN) to facilitate the high-quality reconstruction of THz ptychography. UPNN does not require pretraining and takes only a single dataset as input, which guides the inference process of neural networks by integrating real-world physical models. Therefore, UPNN is more flexible to different types of imaging objects and hardware than the pretraining approach. Moreover, the experimental results and simulated analysis prove that UPNN outperforms conventional iterative ptychography algorithms in terms of image quality, lateral resolution, and robustness.