Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels

Matias Tassano, Charles Laroche, Eva Coupeté, Andrés Almansa

Publication date: 19 October 2022

Abstract: Plug & Play methods combine proximal algorithms with denoiser priors to solve inverse problems. These methods rely on the computability of the proximal operator of the data fidelity term. In this paper, we propose a Plug & Play framework based on linearized ADMM that allows us to bypass the computation of intractable proximal operators. We demonstrate the convergence of the algorithm and provide results on restoration tasks such as super-resolution and deblurring with non-uniform blur.

Has companion code repository: https://github.com/claroche-r/pnp_ladmm

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