Low-light phase retrieval with implicit generative priors
<|reference_start|>Low-light phase retrieval with implicit generative priors: Phase retrieval (PR) is fundamentally important in scientific imaging and is crucial for nanoscale techniques like coherent diffractive imaging (CDI). Low radiation dose imaging is essential for applications involving radiation-sensitive samples. However, most PR methods struggle in low-dose scenarios due to high shot noise. Recent advancements in optical data acquisition setups, such as in-situ CDI, have shown promise for low-dose imaging, but they rely on a time series of measurements, making them unsuitable for single-image applications. Similarly, data-driven phase retrieval techniques are not easily adaptable to data-scarce situations. Zero-shot deep learning methods based on pre-trained and implicit generative priors have been effective in various imaging tasks but have shown limited success in PR. In this work, we propose low-dose deep image prior (LoDIP), which combines in-situ CDI with the power of implicit generative priors to address single-image low-dose phase retrieval. Quantitative evaluations demonstrate LoDIP's superior performance in this task and its applicability to real experimental scenarios.<|reference_end|>
@article{manekar2024low-light,
    title={Low-light phase retrieval with implicit generative priors},
    author={Raunak Manekar, Elisa Negrini, Minh Pham, Daniel Jacobs, Jaideep
  Srivastava, Stanley J. Osher, Jianwei Miao},
    journal={arXiv preprint arXiv:2402.17745},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17745},
    primaryClass={physics.comp-ph cs.CV physics.optics}
}