A nonmonotone adaptive projected gradient method for primal-dual total variation image restoration

Abstract

This paper studies image restoration problems from noisy and blurred observation. Based on a primal-dual total variation model, a nonmonotone adaptive projected gradient method is proposed and tested. By introducing an auxiliary variable, the proposed method implements image restoration by de-blurring and de-noising alternatively at each iteration. Convergence result of the proposed method is established. Numerical results illustrate the efficiency of this method and indicate that it is competitive to some state-of-the-art algorithms in the literature, such as FISTA and FTVd.