  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.
