Image super resolution using saliency-modulated context-aware sparse decomposition

This paper presents a novel saliency-modulated sparse representation algorithm for image super resolution. In images, regions salient to human eyes appear to be more organized and structured. This property is utilized in both the dictionary learning and the sparse coding process to capture more structural details for the reconstructed image. Apart from a general dictionary, example patches from the salient regions are extracted to train a salient dictionary. We also incorporate context-aware sparse decomposition to model dependencies between dictionary atoms of adjacent patches, especially in the salient regions. Experiments show the proposed method outperforms state-of-the-art methods with the highest PSNR gain. Subjective results demonstrate the proposed method reduces artifacts and preserves more details.