Single Image Super-Resolution via Residual Dictionary Learning

Aiming at the shortcomings of traditional learning-based super-resolution (SR) reconstruction algorithms, single image super-resolution via residual dictionary learning is proposed. This method adds residual image learning to the super-resolution algorithm of beta process joint dictionary learning for coupled feature spaces. The residual dictionary pairs are learned by combining the high-resolution (HR) and low-resolution (LR) images in the external training set, which can improve the reconstruction quality and speed up the dictionary training. According to the experimental results, compared with these traditional algorithms, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of the proposed algorithm are significantly improved, and the visual effect is also improved.