LoDIP: Low light phase retrieval with deep image prior

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.

Phase Retrieval using Single-Instance Deep Generative Prior

Several deep learning methods for phase retrieval exist, but most of them fail on realistic data without precise support information. We propose a novel method based on single-instance deep generative prior that works well on complex-valued crystal data.