Hoidn Oliver, Mishra Aashwin Ananda, Mehta Apurva
SLAC National Accelerator Laboratory, Menlo Park, CA, USA.
Sci Rep. 2023 Dec 21;13(1):22789. doi: 10.1038/s41598-023-48351-7.
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 gains a factor of 4 in linear resolution and an 8 dB improvement in PSNR while also accruing improvements in generalizability and robustness. This blend of performance and computational efficiency 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.
通过规避光学分辨率的限制，相干衍射成像（CDI）和叠层成像正在进入从X射线成像到天文学等科学领域。然而，耗时的迭代相位恢复需求阻碍了实时成像。虽然有监督的深度学习策略提高了重建速度，但它们牺牲了图像质量。此外，这些方法对大量标记训练数据的需求在实验上是繁重的。在这里，我们提出了一种无监督的物理信息神经网络重建方法，即叠层成像物理信息神经网络（PtychoPINN），它在将衍射前向映射与重叠测量的实空间约束相结合以提高重建质量的同时，保持了基于深度学习的重建速度提高100到1000倍的优势。特别是，PtychoPINN在线性分辨率上提高了4倍，在峰值信噪比（PSNR）上提高了8 dB，同时在通用性和鲁棒性方面也有所改进。这种性能与计算效率的融合为X射线自由电子激光（XFEL）和衍射极限光源等高通量环境下的高分辨率实时成像提供了令人兴奋的前景。
