Multimedia Tools and Applications

, Volume 75, Issue 7, pp 4115–4128 | Cite as

Image super-resolution base on multi-kernel regression

  • Jianmin Li
  • Yanyun QuEmail author
  • Cuihua Li
  • Yuan Xie


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.


Super resolution Kernel regression Multi kernel learning 



This research work is support by the National Natural Science Foundation of China Under Grant No. 61373077 and Grant No.61402480, the Natural Science Foundation of Fujian Province of China Under Grant No. 2013J01257, and the Scientific Research Foundation for the Introduction of Talent at Xiamen University of Technology No. YKJ12023R.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Computer Science DepartmentXiamen UniversityXiamenPeople’s Republic of China
  2. 2.The State Key Lab of Complex Systems and Intelligence Science, Institute of AutomationChinese Academy of SciencesBeijingPeople’s Republic of China

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