  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 factor of
100-to-1000 speedup 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 (with a typical 10 dB PSNR
increase), and linear resolution (2- 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.
