A fast proximal splitting algorithm for constrained TGV-regularized image restoration

In this paper, a fast algorithm is proposed to tackle the constrained total generalized variation (TGV) based image restoration problem. The proposed algorithm proceeds by splitting: the nonsmooth constrained TGV model is first decomposed into several subproblems easier to solve; then the linear gradient or proximity operators, including projections and shrinkages, of the subproblems are individually called without inner iteration. The algorithm is highly parallel since most of its steps can be executed simultaneously. Image restoration experiments demonstrate that the proposed algorithm outperforms several state-of-the-art methods both in speed and accuracy, while efficiently suppressing staircase effects and presenting better visual impression.