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Edge and color preserving single image superresolution

[+] Author Affiliations
Songze Tang

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

Liang Xiao

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

Jun Zhang

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

Lili Huang

Guangxi University of Science and Technology, Faculty of Science, Liuzhou 545006, China

J. Electron. Imaging. 23(3), 033002 (May 06, 2014). doi:10.1117/1.JEI.23.3.033002
History: Received November 11, 2013; Revised March 14, 2014; Accepted March 28, 2014
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Abstract.  Most existing superresolution (SR) techniques focus primarily on improving the quality in the luminance component of SR images, while paying less attention to the chrominance component. We present an edge and color preserving image SR approach. First, for the luminance channel, a heavy-tailed gradient distribution of natural images is investigated as an image prior. Then, an efficient optimization algorithm is developed to recover the latent high-resolution (HR) luminance component. Second, for the chrominance channels, we propose a two-stage framework for luminance-guided chrominance SR. In the first stage, since most of the shape and structural information is contained in the luminance channel, a simple Markov random field formulation is introduced to search the optimal direction for color local interpolation guided by HR luminance components. To further improve the quality of the chrominance channels, in the second stage, a nonlocal auto regression model is utilized to refine the initial HR chrominance. Finally, we combine the SR reconstructed luminance components with the generated HR chrominance maps to get the final SR color image. Systematic experimental results demonstrated that our method outperforms some state-of-the-art methods in terms of the peak signal-to-noise ratio, structural similarity, feature similarity, and the mean color errors.

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Citation

Songze Tang ; Liang Xiao ; Pengfei Liu ; Jun Zhang and Lili Huang
"Edge and color preserving single image superresolution", J. Electron. Imaging. 23(3), 033002 (May 06, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.3.033002


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