Joint group dictionary-based structural sparse representation for image restoration

Abstract

With the aid of image nonlocal self-similarity (NSS), recent studies have indicated that structural sparse representation (SSR) models with patch groups as processing units have a great potential in various image inverse problems. Most of existing works, however, only use the dictionary learned from internal or external patch groups for sparse representation. Consequently, such methods generally overfit degradation or do not adapt to the given image. In this paper, we propose a novel sparse representation model, termed joint group dictionary-based SSR (JGD-SSR) model, to alleviate the above problem. Compared with traditional SSR models, the proposed JGD-SSR provides an effective mechanism to exploit internal and external dictionaries jointly. Moreover, to make the proposed model more robust, the connection between JGD-SSR and maximum a posteriori (MAP) estimation is revealed to adaptively estimate regularization parameters. When we apply the proposed JGD-SSR to image restoration tasks, an alternating minimization scheme is developed to solve the resulting optimization problems. The experimental results, including image denoising, deblocking, and deblurring, demonstrate that the proposed JGD-SSR-based restoration algorithm outperforms many state-of-the-art competing methods in terms of both objective and visual qualities.