Hypergraph-regularized sparse representation for single color image super resolution

Sparsity-based single image super resolution method generates the High-Resolution (HR) output via a corresponding dictionary from the Low-Resolution (LR) input. However, most of these existing methods ignore the complementary information from color channels, which causes the loss of a valid prior and the limitation of HR image quality improvement. In this paper, hypergraph regularization is first incorporated with Joint Color Dictionary Training (JCDT) model and HR image reconstruction (HRIR) model. A novel Hypergraph-regularized Sparse coding-based Super Resolution (HG-ScSR) is proposed. This regularization can not only focus on the illuminance information, but also exploit the self-channel and cross-channel information of three color RGB channels from high-resolution image patches. Especially, the complex relationship is explored among every color image patch pixel and the consistency of the similar pixels is enforced. Both simulated and real data experiments verify the higher performance of the proposed HG-ScSR.