Resolution enhancement based on learning the sparse association of image patches

Example-based image super-resolution techniques model the co-occurrence patterns between the middle and high frequency layers of example images to estimate the missing high frequency component for low resolution input. However, many existing approaches seek to estimate the optimal solution within a small set of candidates by using empirical criteria. Hence their representational performance is limited by the quality of the candidate set, and the generated super-resolution image is unstable, with noticeable artifacts. In this paper, we propose a novel image super-resolution method based on learning the sparse association between input image patches and the example image patches. We improve an existing sparse-coding algorithm to find sparse association between image patches. We also propose an iterative training strategy to learn a redundancy reduced basis set to speed up the super-resolution process. Comparing to existing example-based approaches, the proposed method significantly improves image quality, and the produced super-resolution images are sharp and natural, with no obvious artifact.