  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.
