Learning-Based Image Restoration for Compressed Image through Neighboring Embedding

In this paper, we propose a novel learning-based image restoration scheme for compressed images by suppressing compression artifacts and recovering high frequency components with the priors learned from a training set of natural images. Specifically, Deblocking is performed to alleviate the blocking artifacts. Moreover, consistency of the primitives is enhanced by estimating the high frequency components, which are simply truncated during quantization. Furthermore, with the assumption that small image patches in the enhanced and real high frequency images form manifolds with similar local geometry in the corresponding image feature spaces, a neighboring embedding-based mapping strategy is utilized to reconstruct the target high frequency components. And experimental results have demonstrated that the proposed scheme can reproduce higher-quality images in terms of visual quality and PSNR, especially the regions relating to the contours.