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Proceedings Paper

Single face image reconstruction for super resolution using support vector regression
Author(s): Haijie Lin; Qiping Yuan; Zhihong Chen; Xiaoping Yang
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Paper Abstract

In recent years, we have witnessed the prosperity of the face image super-resolution (SR) reconstruction, especially the learning-based technology. In this paper, a novel super-resolution face reconstruction framework based on support vector regression (SVR) about a single image is presented. Given some input data, SVR can precisely predict output class labels. We regard the SR problem as the estimation of pixel labels in its high resolution version. It’s effective to put local binary pattern (LBP) codes and partial pixels into input vectors during training models in our work, and models are learnt from a set of high and low resolution face image. By optimizing vector pairs which are used for learning model, the final reconstructed results were advanced. Especially to deserve to be mentioned, we can get more high frequency information by exploiting the cyclical scan actions in the process of both training and prediction. A large number of experimental data and visual observation have shown that our method outperforms bicubic interpolation and some stateof- the-art super-resolution algorithms.

Paper Details

Date Published: 31 October 2016
PDF: 8 pages
Proc. SPIE 10020, Optoelectronic Imaging and Multimedia Technology IV, 100201D (31 October 2016); doi: 10.1117/12.2247421
Show Author Affiliations
Haijie Lin, Tianjin Univ. of Technology (China)
Qiping Yuan, Tianjin Univ. of Technology (China)
Zhihong Chen, Tianjin Univ. of Technology (China)
Xiaoping Yang, Tianjin Univ. of Technology (China)

Published in SPIE Proceedings Vol. 10020:
Optoelectronic Imaging and Multimedia Technology IV
Qionghai Dai; Tsutomu Shimura, Editor(s)

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