Single image super‐resolution based on sparse representation using edge‐preserving regularization and a low‐rank constraint

Abstract

Sparse representation-based non-local self-similarity approaches have demonstrated promising performance in single image super-resolution reconstruction. This type of method, however, cannot always effectively preserve the key details of an image, resulting in edge artifacts and local structure blurring. To better preserve the image edge information, in this paper, a sparse representation super-resolution method based on non-local self-similarity is proposed. First, we impose slide window gradient domain guided filtering on both the low-resolution input image and the degraded restored image. Then, we utilize their difference as an edge-preserving regularization term and incorporate this regularization term into the non-local self-similarity-based sparse representation model to build a sparse coding model, which can enhance the restored high-resolution image patches’ detail information. Finally, the iterative threshold algorithm is used to calculate the sparse representation coefficients so that a high-resolution image can be estimated. Furthermore, to explore the potential structures of the subspaces spanned by similar patches, we enforce a low-rank matrix recovery technique on the generated super-resolution image, which can further refine the reconstruction quality. Experimental results prove that the new approach preserves the critical edge structures while suppressing noise and exceeding some popular methods both quantitatively and qualitatively.