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《Opto-Electronic Engineering》 2016-11
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Single Image Super-resolution Reconstruction via Supervised Multi-dictionary Learning

WU Congzhong;HU Changsheng;ZHANG Mingjun;XIE Zhenzhu;ZHAN Shu;School of Computer and Information, Hefei University of Technology;  
In order to overcome the problems that the dictionary training process is time-consuming and the reconstruction quality couldn't meet the applications, we propose a super resolution reconstruction algorithm which based on a supervised KSVD multi-dictionary learning and class-anchored neighborhood regression. Firstly, the Gaussian mixture model clustering algorithm is employed to cluster the low resolution training features; Then we use the supervised KSVD algorithm to generate each subclass dictionary and a discriminative-linear classifier simultaneously; Finally, each input feature block is categorized by the classifier and reconstructed by the corresponding subclass dictionary and class-anchored neighborhood regression. Experimental results show that our method obtains a better result both on subjective and objective compare with other methods, and has a better adaptability to face image.
【Key Words】: Gaussian mixture model supervised dictionary learning super-resolution sparse representation
【Fund】: 国家自然科学基金面上项目(61371156);; 安徽省科技攻关项目(1401B042019)
【CateGory Index】: TP391.41
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