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.

翻译：相位恢复（PR）在科学成像中具有根本重要性，对于相干衍射成像（CDI）等纳米尺度技术至关重要。低辐射剂量成像对于涉及辐射敏感样品的应用至关重要。然而，由于高散粒噪声，大多数相位恢复方法在低剂量场景中表现不佳。光学数据采集装置（如原位CDI）的最新进展为低剂量成像带来了希望，但它们依赖于时间序列测量，因此不适用于单图像应用。同样，数据驱动的相位恢复技术也难以适应数据稀缺的情况。基于预训练和隐式生成先验的零样本深度学习方法在各种成像任务中取得了成效，但在相位恢复中取得的成功有限。在本工作中，我们提出了低剂量深度图像先验（LoDIP），它将原位CDI与隐式生成先验的能力相结合，以解决单图像低剂量相位恢复问题。定量评估证明了LoDIP在此任务中的卓越性能及其在真实实验场景中的适用性。