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Image super-resolution base on multi-kernel regression

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Abstract

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.

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Acknowledgments

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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Authors and Affiliations

  1. Computer Science Department, Xiamen University, Xiamen, People’s Republic of China

    Jianmin Li, Yanyun Qu & Cuihua Li

  2. The State Key Lab of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China

    Yuan Xie

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  1. Jianmin Li
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  2. Yanyun Qu
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  3. Cuihua Li
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  4. Yuan Xie
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Correspondence to Yanyun Qu.

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Li, J., Qu, Y., Li, C. et al. Image super-resolution base on multi-kernel regression. Multimed Tools Appl 75, 4115–4128 (2016). https://doi.org/10.1007/s11042-015-3016-4

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  • DOI: https://doi.org/10.1007/s11042-015-3016-4

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