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 highresolution 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.