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Proceedings Paper

Color image super-resolution algorithm based on SVM classified learning
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Paper Abstract

Due to the limitations of image capture device and imaging environments in traditional imaging process, high-resolution (HR) images are difficult to be obtained. The method of digital image processing can be used in image super-resolution with one or an image sequence in original conditions to reconstruct HR images which over the range of imaging system. Traditional learning-based super-resolution algorithm need to run through the sample library with a high computing complexity, and a high recognition rate in the scene with small shifts. This dissertation is mainly about color image SR and parallel implementation of the SR algorithm. An algorithm based on SVM classified learning is proposed in this paper.

Paper Details

Date Published: 24 October 2017
PDF: 13 pages
Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 1046245 (24 October 2017); doi: 10.1117/12.2285456
Show Author Affiliations
Jianfei Li, Tianjin Univ. of Technology (China)
Xiaoping Yang, Tianjin Univ. of Technology (China)
Zhihong Chen, Tianjin Univ. of Technology (China)
Haifeng Yang, Tianjin Univ. of Technology (China)
Jun Liu, Tianjin Univ. of Technology (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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