  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.
