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《Journal of Data Acquisition and Processing》 2018-02
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Single Image Super-Resolution from Local Self-examples Based on an Improved Similarity Measurement Model

Zhao Liling;Sun Quansen;School of Computer Science and Technology,Nanjing University of Science and Technology;School of Information and Control,Nanjing University of Information Science & Technology;  
The accurate matching of high and low resolution image blocks is the key of self-examples super resolution algorithm.In the process of blocks matching of high and low resolution images,considering the importance of texture image block structure,a similarity metric model based on constrained texture image patch is proposed in this paper.By using this exact matching model,the detail of super-resolution result image is further enriched,and the image quality is improved also.The new algorithm has the particular advantage of improving spatial resolution of image only using prior information of single low-resolution image itself.The experimental results show that the proposed algorithm has a better super-resolution visual effect compared with the bicubic interpolation algorithm and the local self-examples super-resolution algorithm,and it also has a good performance in the objective evaluation index.
【Fund】: 国家自然科学基金(61273251)资助项目
【CateGory Index】: TP391.41
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