Phase retrieval (PR) is a fundamental challenge in scientific imaging,
enabling nanoscale techniques like coherent diffractive imaging (CDI). Imaging
at low radiation doses becomes important in applications where samples are
susceptible to radiation damage. However, most PR methods struggle in low dose
scenario due to the presence of very high shot noise. Advancements in the
optical data acquisition setup, exemplified by in-situ CDI, have shown
potential for low-dose imaging. But these depend on a time series of
measurements, rendering them unsuitable for single-image applications.
Similarly, on the computational front, data-driven phase retrieval techniques
are not readily adaptable to the single-image context. Deep learning based
single-image methods, such as deep image prior, have been effective for various
imaging tasks but have exhibited limited success when applied to PR. In this
work, we propose LoDIP which combines the in-situ CDI setup with the power of
implicit neural priors to tackle the problem of single-image low-dose phase
retrieval. Quantitative evaluations demonstrate the superior performance of
LoDIP on this task as well as applicability to real experimental scenarios.

LoDIP 结合 in-situ CDI 设置和隐式神经先验的能力，解决了单图低剂量相位恢复问题，通过定量评估证明了其卓越性能以及在实际实验场景中的适用性。

低光相位恢复与深层图像先验

LoDIP: Low light phase retrieval with deep image prior

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。