Li AO, Li YIBING^*, Yang XIAODONG, and Liu YUE

Institute of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China

(Received September 2, 2013; Accepted September 29, 2013)

In order to utilizing the local and non-local information in the image, we proposed a novel sparse scheme for image restoration in this paper. The new scheme includes two important contributions. The first one is that we extended the image prior model in conventional total variation to the dual-prior models for combining the local smoothness and non-local sparsity under regularization framework. The second one is we developed a modified iterative Split Bregman majorization method to solve the objective function with dual-prior models. The experimental results show that the proposed scheme achieved the state-of-the-art performance compared to the current restoration algorithms.

Key words: image restoration, non-local sparsity, Split Bregman, total variation, regularization

^*E-mail address: liyibing0920@sina.cn