Solving Inverse Problems with a Flow-based Noise Model

• URL: http://arxiv.org/abs/2003.08089v3
• Date: Thu, 1 Jul 2021 07:46:59 GMT
• Title: Solving Inverse Problems with a Flow-based Noise Model
• Authors: Jay Whang, Qi Lei, Alexandros G. Dimakis
• Abstract summary: We study image inverse problems with a normalizing flow prior. Our formulation views the solution as the maximum a posteriori estimate of the image conditioned on the measurements. We empirically validate the efficacy of our method on various inverse problems, including compressed sensing with quantized measurements and denoising with highly structured noise patterns.
• Score: 100.18560761392692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study image inverse problems with a normalizing flow prior. Our formulation views the solution as the maximum a posteriori estimate of the image conditioned on the measurements. This formulation allows us to use noise models with arbitrary dependencies as well as non-linear forward operators. We empirically validate the efficacy of our method on various inverse problems, including compressed sensing with quantized measurements and denoising with highly structured noise patterns. We also present initial theoretical recovery guarantees for solving inverse problems with a flow prior.

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