Image super-resolution using dictionaries and self-similarity

Image super resolution attempts to extract a high resolution image using one or more corrupted low resolution images. Typical sparse dictionary based super resolution methods remove the undesired effects but may not significantly enhance resolution. In contrast, methods that exploit local self-similarity enhance the native resolution as well as the undesired artifacts present in the low resolution image. In this paper, we propose a novel single image super resolution approach that renders high resolution images by exploiting dictionary based non-local methods and uses local similarity of small spatial patches of the image to eliminate undesired artifacts. Our quantitative results on several test datasets are promising.