Subspace Correction Methods for a Class of Nonsmooth and Nonadditive Convex Variational Problems with Mixed $L^1/L^2$ Data-Fidelity in Image Processing

Abstract

The minimization of a functional composed of a nonsmooth and nonadditive regularization term and a combined $L^1$ and $L^2$ data-fidelity term is proposed. It is shown analytically and numerically that the new model has noticeable advantages over popular models in image processing tasks. For the numerical minimization of the new objective, subspace correction methods are introduced which guarantee the convergence and monotone decay of the associated energy along the iterates. Moreover, an estimate of the distance between the outcome of the subspace correction method and the global minimizer of the nonsmooth objective is derived. This estimate and numerical experiments for image denoising, inpainting, and deblurring indicate that in practice the proposed subspace correction methods indeed approach the global solution of the underlying minimization problem.