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.

翻译：插件 & Play 方法将近似算法与 Exnoiser 前置法相结合, 以解决反向问题 。 这些方法依赖于数据忠实术语的准操作员的可计算性 。 在本文中, 我们提议基于线性化 ADMM 的 Plug & Play 框架, 使我们能够绕过难以操作的准操作员的计算 。 我们演示了算法的趋同, 并提供恢复任务的结果, 如超分辨率和与非统一模糊分解 。