By circumventing the resolution limitations of optics, coherent diffractive
imaging (CDI) and ptychography are making their way into scientific fields
ranging from X-ray imaging to astronomy. Yet, the need for time consuming
iterative phase recovery hampers real-time imaging. While supervised deep
learning strategies have increased reconstruction speed, they sacrifice image
quality. Furthermore, these methods' demand for extensive labeled training data
is experimentally burdensome. Here, we propose an unsupervised physics-informed
neural network reconstruction method, PtychoPINN, that retains the intrinsic
speed of deep learning-based reconstruction while improving reconstruction
quality by combining the diffraction forward map with real-space constraints
from overlapping measurements. In particular, PtychoPINN significantly advances
generalizability, accuracy (evidenced by a 10 dB PSNR increase), and linear
resolution (with a 3- to 6-fold gain). This blend of performance and speed
offers exciting prospects for high-resolution real-time imaging in
high-throughput environments such as X-ray free electron lasers (XFELs) and
diffraction-limited light sources.

使用非监督物理知识神经网络重建方法，结合衍射正演映射与重叠测量的约束，提高了重构速度和质量，为高通量环境下的高分辨率实时成像带来了令人兴奋的前景。