Super-resolution for low quality thumbnail images

This paper proposes a single-image super-resolution scheme for enlarging low quality thumbnail images widely distributed on the web, which are often generated by downsampling plus compression. To obtain visually pleasurable high-resolution versions for this kind of low-resolution images, we first adopt a PDE-based image regularization technique to alleviate the compression noise in the distorted thumbnails, and then use learning-based pair matching to further enhance the high-frequency details in the upsampled images. Experimental results show that our solution achieves better visual quality for both offline and online test images, compared with traditional methods.