Image super-resolution base on multi-kernel regression

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• 作者：Jianmin Li ; Yanyun Qu ; Cuihua Li ; Yuan Xie
• 关键词：Super resolution ; Kernel regression ; Multi kernel learning
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：75
• 期：7
• 页码：4115-4128
• 全文大小：950 KB
• 参考文献：1.Chang H, Yeung D-Y, Xiong Y (2004) Super-resolution through neighbor embedding, in Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 I.E. Computer Society Conference on:I-I
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• 作者单位：Jianmin Li (1)
  Yanyun Qu (1)
  Cuihua Li (1)
  Yuan Xie (2)
  
  1. Computer Science Department, Xiamen University, Xiamen, People’s Republic of China
  2. The State Key Lab of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China
  
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

In this paper, a novel approach to single image super-resolution based on the multi-kernel regression is presented. This approach focuses on learning the map between the space of high-resolution image patches and the space of blurred high-resolution image patches, which are the interpolation results generated from the corresponding low-resolution images. Kernel regression based super-resolution approaches are promising, but kernel selection is a critical problem. In order to avoid demanding and time-consuming cross validation for kernel selection, we propose multi-kernel regression (MKR) model for image Super-Resolution (SR). Considering the multi-kernel regression model is prohibited when the training data is large-scale, we further propose a prototype MKR algorithm which can reduce the computational complexity. Extensive experimental results demonstrate that our approach is effective and achieves a high quality performance in comparison with other super-resolution methods.