数据采集与处理2018，Vol.33Issue(2)：240-247,8.DOI:10.16337/j.1004-9037.2018.02.006
改进相似性度量模型的单幅图像自学习超分辨算法
Single Image Super-Resolution from Local Self-examples Based on an Improved Similarity Measurement Model
摘要
Abstract
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.关键词
相似性度量/方差/自学习/单幅图像/超分辨率Key words
similarity measure/variance/self-example/single image/super-resolution分类
信息技术与安全科学引用本文复制引用
赵丽玲,孙权森..改进相似性度量模型的单幅图像自学习超分辨算法[J].数据采集与处理,2018,33(2):240-247,8.基金项目
国家自然科学基金(61273251)资助项目. （61273251）
