Single image super resolution using local smoothness and nonlocal self-similarity priors

• A reconstruction-based single image super resolution method is presented.
• Local smoothness and nonlocal self-similarity priors are incorporated in our model.
• The Split Bregman Iteration is imitated to solve the L1-regularized problem.
• The proposed method can achieve higher quality results.

Single image super resolution (SISR) is an inverse problem, so an effective image prior is necessary to reconstruct a high resolution (HR) image from a single low resolution (LR) image. On the one hand, natural images satisfy the property of local smoothness; on the other hand, the patches could find some similar patches in different locations within the same image, and this property is known as nonlocal self-similarity. In this paper, we propose a SISR method by incorporating the local smoothness and nonlocal self-similarity priors in the reconstruction-based SISR framework simultaneously, and the Split Bregman Iteration (SBI) optimization algorithm is imitated to solve the L1-regularized problem. Experimental results show that, in most case, the proposed method quantitatively and qualitatively outperforms the state-of-the-art SISR algorithms.