Abstract
Arguably the most common cause of image degradation is compression.This papers presents a novel approach to restoring JPEG-compressed images.The main innovation is in the approach of exploiting residual redundancies of JPEG code streams and sparsity properties of latent images.The restoration is a sparse coding process carried out joint-ly in the DCT and pixel domains.The prowess of the pro-posed approach is directly restoring DCT coefficients of the latent image to prevent the spreading of quantization errors into the pixel domain,and at the same time using on-line machine-learnt local spatial features to regulate the solu-tion of the inderlying inverse problem.Experimental results are encouraging and show the promise of the new approach in significantly improving the quality of DCT-coded images.