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

DOI:
10.60864/m4s8-6b76
Citation Author(s):
Charles Laroche, Andres Almansa, Eva Coupeté, Matias Tassano
Submitted by:
Charles Laroche
Last updated:
17 November 2023 - 12:07pm
Document Type:
Presentation Slides
Document Year:
2023
Event:
Presenters:
Charles Laroche
Paper Code:
SS-L12.1
 

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.

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