Deep Equilibrium Architectures for Inverse Problems in Imaging

• URL: http://arxiv.org/abs/2102.07944v1
• Date: Tue, 16 Feb 2021 03:49:58 GMT
• Title: Deep Equilibrium Architectures for Inverse Problems in Imaging
• Authors: Davis Gilton, Gregory Ongie, Rebecca Willett
• Abstract summary: Recent efforts on solving inverse problems in imaging via deep neural networks use architectures inspired by a fixed number of iterations of an optimization method. This paper describes an alternative approach corresponding to an em infinite number of iterations, yielding up to a 4dB PSNR improvement in reconstruction accuracy.
• Score: 14.945209750917483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent efforts on solving inverse problems in imaging via deep neural networks use architectures inspired by a fixed number of iterations of an optimization method. The number of iterations is typically quite small due to difficulties in training networks corresponding to more iterations; the resulting solvers cannot be run for more iterations at test time without incurring significant errors. This paper describes an alternative approach corresponding to an {\em infinite} number of iterations, yielding up to a 4dB PSNR improvement in reconstruction accuracy above state-of-the-art alternatives and where the computational budget can be selected at test time to optimize context-dependent trade-offs between accuracy and computation. The proposed approach leverages ideas from Deep Equilibrium Models, where the fixed-point iteration is constructed to incorporate a known forward model and insights from classical optimization-based reconstruction methods.

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