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Proceedings Paper

Image super-resolution reconstruction based on residual dictionary learning by support vector regression
Author(s): Jianfei Li; Xiaoping Yang; Zhihong Chen; Jun Liu; Hao Sun
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Paper Abstract

The traditional algorithms of image super-resolution reconstruction are not effective enough to be used in reconstructing high-frequency information of an image. In order to improve the quality of image reconstruction and restore more high-frequency information, the residual dictionary is introduced which can capture the high-frequency information of images such as the edges, angles and corners. The common dictionary is generated by training and learning pairs of low-resolution and high-resolution images. The dictionary combined by common dictionary and residual dictionary is obtained in which more high-frequency information of the images can be restored while the spatial structure of images can be preserved well. The processing of training and testing dictionary is conducted by Support Vector Regression (SVR). Compared with other algorithms in experiments, the proposed method improves its PSNR and SSIM by 3% ~ 4% and 2% ~ 3% on some different images respectively.

Paper Details

Date Published: 2 November 2018
PDF: 9 pages
Proc. SPIE 10817, Optoelectronic Imaging and Multimedia Technology V, 108170U (2 November 2018); doi: 10.1117/12.2500552
Show Author Affiliations
Jianfei Li, Tianjin Univ. of Technology (China)
Xiaoping Yang, Tianjin Univ. of Technology (China)
Zhihong Chen, Tianjin Univ. of Technology (China)
Jun Liu, Tianjin Univ. of Technology (China)
Hao Sun, Tianjin Univ. of Technology (China)


Published in SPIE Proceedings Vol. 10817:
Optoelectronic Imaging and Multimedia Technology V
Qionghai Dai; Tsutomu Shimura, Editor(s)

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