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Journal of Electronic Imaging

Adaptive Charbonnier superresolution method with robust edge preservation capabilities
Author(s): Baraka Maiseli; Qiang Liu; Ogada A. Elisha; Huijun Gao
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Paper Abstract

Superresolution (SR) is known to be an ill-posed inverse problem, which may be solved using some regularization techniques. We have proposed an adaptive regularization method, based on a Charbonnier nonlinear diffusion model to solve an SR problem. The proposed model is flexible because of its automatic capability to reap the strengths of either linear isotropic diffusion, Charbonnier model, or semi-Charbonnier model, depending on the local features of the image. On the contrary, the models proposed from other research works are fixed and hence less feature dependent. This makes such models insensitive to local structures of the images, thereby producing poor reconstruction results. Empirical results obtained from experiments, and presented here, show that the proposed method produces superresolved images which are more natural and contain well-preserved and clearly distinguishable image structures, such as edges. In comparison with other methods, the proposed method demonstrates higher performance in terms of the quality of images it generates.

Paper Details

Date Published: 16 December 2013
PDF: 13 pages
J. Electron. Imag. 22(4) 043027 doi: 10.1117/1.JEI.22.4.043027
Published in: Journal of Electronic Imaging Volume 22, Issue 4
Show Author Affiliations
Baraka Maiseli, Harbin Institute of Technology (China)
Qiang Liu, Harbin Institute of Technology (China)
Ogada A. Elisha, Harbin Institute of Technology (China)
Huijun Gao, Harbin Institute of Technology (China)

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