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Proceedings Paper

Single-image super-resolution based on sparse kernel ridge regression
Author(s): Fanlu Wu; Xiangjun Wang
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Paper Abstract

Because they are affected by imaging conditions, aliasing, noise, etc, imaging systems are unable to obtain all of the information contained in an original scene. Super-resolution (SR) reconstruction is important for the application of image data to increase the resolution of images. In this article, an example-based algorithm is proposed to implement SR reconstruction by single-image. The mapping function between low-resolution (LR) and high-resolution (HR) images is learned by using the method of regularized regression. Then, finding the optimal sparse subset of the training data set by kernel matching pursuit (KMP). The results show that this method can recover detailed information of images, and the computational cost is reduced compared to other example-based SR methods.

Paper Details

Date Published: 24 October 2017
PDF: 6 pages
Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 1046203 (24 October 2017); doi: 10.1117/12.2281290
Show Author Affiliations
Fanlu Wu, Tianjin Univ. (China)
Xiangjun Wang, Tianjin Univ. (China)


Published in SPIE Proceedings Vol. 10462:
AOPC 2017: Optical Sensing and Imaging Technology and Applications
Yadong Jiang; Haimei Gong; Weibiao Chen; Jin Li, Editor(s)

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