Abstract

This talk deals with a novel iterative procedure for multichannel image and signal reconstruction using Bregman distances. With the motivation that multiple channels sharing a common subgradient with respect to a suitable regularization implies desirable properties such as a common edge set (and a common direction of the normals to the level lines) in the case of the total variation (TV), we propose to determine each iterate by regularizing each channel with a weighted linear combination of Bregman distances to all other image channels from the previous iteration. In this sense we generalize the Bregman iteration proposed by Osher et al. in 2005 to multichannel images.