Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements. Since the problem is ill-posed, the recovery requires prior knowledge on the unknown image. We present DOLPH as a new deep model-based architecture for phase retrieval that integrates an image prior specified using a diffusion model with a nonconvex data-fidelity term for phase retrieval. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLPH reconstructs high-quality solutions by alternating data-consistency updates with the sampling step of a diffusion model. Our numerical results show the robustness of DOLPH to noise and its ability to generate several candidate solutions given a set of measurements.
@article{arxiv.2211.00529,
title = {DOLPH: Diffusion Models for Phase Retrieval},
author = {Shirin Shoushtari and Jiaming Liu and Ulugbek S. Kamilov},
journal= {arXiv preprint arXiv:2211.00529},
year = {2022}
}