Block-coordinate Plug-And-Play Methods with Armijo-like line-search for Image Restoration
Federica Porta, Simone Rebegoldi, Andrea Sebastiani
TL;DR
The proposed block-coordinate Plug-and-Play methods are based on a block-coordinate forward-backward framework for solving non-convex and non-separable composite optimization problems, making them particularly suitable for large-scale and resource-constrained imaging applications.
Abstract
In this paper, we develop a class of block-coordinate Plug-and-Play (PnP) methods to address imaging inverse problems. The block-coordinate strategy is designed to reduce the high memory consumption arising in PnP methods that rely on Gradient Step denoisers, whose implementation typically requires storing large computational graphs. The proposed methods are based on a block-coordinate forward-backward framework for solving non-convex and non-separable composite optimization problems. Furthermore, such 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-Łojasiewicz 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.