Block-coordinate Plug-And-Play Methods with Armijo-like line-search for Image Restoration

Abstract

In this paper, we develop a class of block-coordinate forward-backward methods for solving non-convex and non-separable composite optimization problems, and specialize them to imaging inverse problems within the Plug-and-Play (PnP) framework based on Gradient Step (GS) denoisers. The block-coordinate strategy is designed to reduce the high memory consumption associated with the computation of GS denoisers, whose implementation typically requires storing large computational graphs. The proposed methods allow for the joint use of inertial acceleration, variable metric strategies, inexact proximal computations, and adaptive steplength selection via an appropriate line-search procedure. Under mild assumptions on the objective function, we establish a sublinear convergence rate and the stationarity of the limit points. Moreover, convergence of the entire sequence of the iterates is guaranteed under a Kurdyka-{\L}ojasiewicz assumption. Numerical experiments on ill-posed imaging problems, including deblurring and super-resolution, demonstrate that the proposed PnP approach achieves state-of-the-art reconstruction quality while substantially reducing GPU memory requirements, making it particularly suitable for large-scale and resource-constrained imaging applications.