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Partial least-squares regression on common feature space for single image superresolution

[+] Author Affiliations
Songze Tang

Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing 210094, China

Liang Xiao

Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing 210094, China

Jiangsu Province Key Laboratory of Spectral Imaging and Intelligent Sensing, Nanjing 210094, China

Pengfei Liu

Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing 210094, China

Huicong Wu

Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing 210094, China

J. Electron. Imaging. 23(5), 053006 (Sep 18, 2014). doi:10.1117/1.JEI.23.5.053006
History: Received June 10, 2014; Revised August 12, 2014; Accepted August 22, 2014
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Abstract.  We proposed a superresolution (SR) method based on example-learning framework. In our framework, the relationship between the output high-resolution (HR) estimation and the HR training images is approximated by the relationship between the low-resolution (LR) test image and the HR training images. To effectively capture the strong correlation between LR and HR images, the LR and HR images are mapped onto a common feature space. Furthermore, in order to maintain their original two-dimensional (2-D) spatial structure, the original LR and HR patches are mapped onto the underlying common feature space using 2-D canonical correlation analysis. Later, the relationship between HR and LR features is established by partial least squares (PLS) with low regression errors on the derived feature space. In addition, a steering kernel regression (SKR) constraint is integrated into patch aggregation to improve the quality of the recovered images. Finally, the effectiveness of our approach is validated by extensive experimental comparisons with several SR algorithms for the natural image superresolution both quantitatively and qualitatively.

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Citation

Songze Tang ; Liang Xiao ; Pengfei Liu and Huicong Wu
"Partial least-squares regression on common feature space for single image superresolution", J. Electron. Imaging. 23(5), 053006 (Sep 18, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.5.053006


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