A Hybrid Structural Sparse Error Model For Image Deblocking

Inspired by the image nonlocal self-similarity (NSS) prior, structural sparse representation (SSR) models exploit each group as the basic unit for sparse representation, which have achieved promising results in various image restoration applications. However, conventional SSR models only consider the group within the input degraded (internal) image to restore the corresponding group of the original image, which may be ineffective in improving the visual quality of the reconstructed image. In this paper, we propose a novel hybrid structural sparse error (HSSE) model for image deblocking. The proposed HSSE model exploits image NSS prior over both the internal image and external image corpus, which can be complementary in both feature space and image plane. Moreover, we develop an alternating minimization with an adaptive parameter setting strategy to solve the proposed HSSE model. Experimental results demonstrate that the proposed HSSE-based image deblocking algorithm outperforms many state-of-the-art image deblocking methods in terms of objective and visual perception.