A MULTISCALE RESIDUAL SOLVER FOR TOTAL VARIATION MODELS

This paper proposes a multiscale residual solver for total variation regularized models. The proposed solver has three important properties. First, the proposed algorithm is theoretically guaranteed to converge. Second, the proposed method can numerically reach the same global optimal solution as the classical methods. This fact is confirmed on image datasets. Third, the proposed solver is faster than the classical methods. For high resolution images, our solver can be three orders of magnitude faster. The proposed method can be adopted in the applications where total variation regularization is imposed, such as denoising, smoothing, optical flow, and inpainting.