DOLPH: Diffusion Models for Phase Retrieval

• URL: http://arxiv.org/abs/2211.00529v2
• Date: Wed, 2 Nov 2022 03:25:20 GMT
• Title: DOLPH: Diffusion Models for Phase Retrieval
• Authors: Shirin Shoushtari, Jiaming Liu, Ulugbek S. Kamilov
• Abstract summary: 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, recovery requires prior knowledge on unknown image. We present as a new deep model-based for phase retrieval that reconstructs an image prior to a diffusion step.
• Score: 19.22990916056669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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