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

Songze Tang; Liang Xiao; Pengfei Liu; Huicong Wu

doi:10.1117/1.JEI.23.5.053006

18 September 2014 Partial least-squares regression on common feature space for single image superresolution

Songze Tang, Liang Xiao, Pengfei Liu, Huicong Wu

Author Affiliations +

Journal of Electronic Imaging, Vol. 23, Issue 5, 053006 (September 2014). https://doi.org/10.1117/1.JEI.23.5.053006

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.

Citation Download Citation

Songze Tang, Liang Xiao, Pengfei Liu, and Huicong Wu "Partial least-squares regression on common feature space for single image superresolution," Journal of Electronic Imaging 23(5), 053006 (18 September 2014). https://doi.org/10.1117/1.JEI.23.5.053006

Published: 18 September 2014

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