  Total variation (TV) is a widely used regularizer for stabilizing the
solution of ill-posed inverse problems. In this paper, we propose a novel
proximal-gradient algorithm for minimizing TV regularized least-squares cost
functional. Our method replaces the standard proximal step of TV by a simpler
alternative that computes several independent proximals. We prove that the
proposed parallel proximal method converges to the TV solution, while requiring
no sub-iterations. The results in this paper could enhance the applicability of
TV for solving very large scale imaging inverse problems.
