Single Image Super Resolution Based on Adaptive Multi-Dictionary Learning
Pan Zong-x · Dianzi xuebao · 2015
Adaptive dictionary learning uses the lowresolution image itself as training samples to make the similar patches have sparse representation over the learned dictionary,so that extra information can be exploited from structural selfsimilarity by dictionary learning. In this paper,we propose a single image super resolution method based on adaptive multidictionary learning. To exploit extra information from both the lowresolution image itself,and the image database,the proposed method incorporates the idea of global dictionary learning that the image database can be used to obtain extra information into the process of adaptive dictionary learning. In the proposed method,all patches in the image pyramid of the lowresolution image are clustered into several groups,then each patch satisfying a certain condition in the database is classified into one of these groups with the supervision of the clustering results,and multi-dictionary learning is used to learn corresponding dictionaries for different groups. Experimental results demonstrate that our method achieves better result compared with ScSR,SISR,NLIBP,CSSS and m SSIMmethods.