标题： DDRM-PR：使用去噪扩散恢复模型的傅里叶相位恢复 显示英文标题

标题： DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models

摘要： 扩散模型已被证明在解决各种逆问题中作为学习先验的有效性。 然而，大多数现有方法仅限于线性逆问题。 本文利用去噪扩散恢复模型（DDRM）的高效且无监督的后验采样框架，用于解决非线性相位恢复问题，这需要从其噪声强度测量值（如傅里叶强度）中重建图像。 该方法将基于模型的交替投影方法与DDRM相结合，以利用预训练的无条件扩散先验进行相位恢复。 通过仿真和实验数据展示了该方法的性能。 结果表明了该方法在改进交替投影方法方面的潜力及其局限性。 显示英文摘要

摘要： Diffusion models have demonstrated their utility as learned priors for solving various inverse problems. However, most existing approaches are limited to linear inverse problems. This paper exploits the efficient and unsupervised posterior sampling framework of Denoising Diffusion Restoration Models (DDRM) for the solution of nonlinear phase retrieval problem, which requires reconstructing an image from its noisy intensity-only measurements such as Fourier intensity. The approach combines the model-based alternating-projection methods with the DDRM to utilize pretrained unconditional diffusion priors for phase retrieval. The performance is demonstrated through both simulations and experimental data. Results demonstrate the potential of this approach for improving the alternating-projection methods as well as its limitations.