Abstract
Highly compressed images are usually not only of low-resolution, but also suffer from compression artifacts, e.g., blocking artifacts; ringing artifact. So if we do image super-resolution (SR) to a highly compressed image directly, we will also simultaneously magnify the artifacts, and get unpleasing visual quality. But we find that if we individually performing deblocking followed by SR to an image, it would lose some image details which may be useful for SR when deblocking, and resulting in worse SR result. If we performing SR followed by deblocking, it will magnify the blocking artifacts, and we will hardly to deblocking well for the SR result, so the result will remain blocking artifact or over smooth. In summary of above, we find that we can't get a good result if we individually performing deblocking and SR, so we want to find a method which can combine SR and deblocking in one operation. In this thesis, we propose a self-learning-based SR framework to simultaneously achieve single-image SR and blocking artifact removal for highly compressed images. In our method, we propose to self-learn image sparse representation for modeling the relationship between low and high-resolution image patches in terms of the learned dictionaries, respectively, for image patches with and without blocking artifacts. As a result, image SR and deblocking can be simultaneously achieved via sparse representation and MCA (morphological component analysis)-based image decomposition. Experimental results demonstrate the efficacy of the proposed algorithm.