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- Title
Image super-resolution base on multi-kernel regression.
- Authors
Li, Jianmin; Qu, Yanyun; Li, Cuihua; Xie, Yuan
- 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.
- Subjects
HIGH resolution imaging; KERNEL (Mathematics); REGRESSION analysis; COMPUTATIONAL complexity; IMAGE processing; SUPPORT vector machines
- Publication
Multimedia Tools & Applications, 2016, Vol 75, Issue 7, p4115
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-015-3016-4