18 September 2014 Partial least-squares regression on common feature space for single image superresolution
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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.
© 2014 SPIE and IS&T
Songze Tang, Songze Tang, Liang Xiao, Liang Xiao, Pengfei Liu, Pengfei Liu, Huicong Wu, 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 . Submission:
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