Gaussian Scale Patch Group Sparse Representation for Image Restoration.

This passage puts forward a new sparse representation method, to solve the shortage problem of image restoration. First of all, extract the patch groups by utilize the non-local similar patches, and then using the simultaneous sparse coding to develop a non-local extension of Gaussian scale mixture model. Finally integrate the patch group model and Gaussian scale mixture model into encoding framework. Experimental results show that the proposed method achieves leading performance in terms of both quantitative measures and visual quality. In addition, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar methods.