论文标题
事实证明，可融合的插头和播放线性化的ADMM，应用于脱毛的空间变化内核
Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels
论文作者
论文摘要
插件方法将近端算法与Denoiser先验结合在一起，以解决反问题。这些方法依赖于数据保真度项的近端运算符的可计算性。在本文中，我们提出了一个基于线性化ADMM的插头框架，该框架使我们能够绕过棘手的近端操作员的计算。我们证明了该算法的收敛性，并为诸如超分辨率和脱毛的恢复任务提供了结果。
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.
