A dual block coordinate proximal algorithm with application to deconvolution of interlaced video sequences

Inverse problems encountered in video processing often require to minimize criteria involving a high number of variables. Among available optimization techniques, proximal methods have shown their efficiency in solving large-scale possibly nonsmooth problems. When some of the proximity operators involved in these methods do not have closed form expressions, they may constitute a bottleneck in terms of computational complexity and memory requirements. In this paper, we address this problem and propose accelerated techniques for solving it. A new dual block-coordinate forward-backward algorithm computing the proximity operator of a sum of convex functions composed with linear operators is proposed and theoretically analyzed. The numerical performance of the approach is assessed through an application to deconvolution and super-resolution of interlaced video sequences.