Learning of context-aware single image super-resolution
We propose a novel learning-based method for single image super-resolution (SR). Given a low-resolution input image and its image pyramid, we advance a context-constrained image segmentation to construct a super-pixel database with different context categories for learning purposes. By utilizing context-specific image sparse representation, our method aims at modeling the relationship between the interpolated image patches and their ground truth pixels from different context categories via support vector regression (SVR). To produce the final SR output, we upsample the low-resolution input, followed by the refinement of each image patch using the SVR models observed from the associated context categories. Unlike prior learning-based SR methods, our approach advances a self-learning technique and does not assume the reoccurrence of image patches (within or across image scales). We do not need to collect training low/high-resolution image data in advance either. Empirical results verify the effectiveness of our SR approach, which quantitatively and qualitatively outperforms existing interpolation or learning-based SR methods in most cases.