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Journal of Electronic Imaging

Image denoising via adaptive eigenvectors of graph Laplacian
Author(s): Ying Chen; Yibin Tang; Ning Xu; Lin Zhou; Li Zhao
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Paper Abstract

An image denoising method via adaptive eigenvectors of graph Laplacian (EGL) is proposed. Unlike the trivial parameter setting of the used eigenvectors in the traditional EGL method, in our method, the eigenvectors are adaptively selected in the whole denoising procedure. In detail, a rough image is first built with the eigenvectors from the noisy image, where the eigenvectors are selected by using the deviation estimation of the clean image. Subsequently, a guided image is effectively restored with a weighted average of the noisy and rough images. In this operation, the average coefficient is adaptively obtained to set the deviation of the guided image to approximately that of the clean image. Finally, the denoised image is achieved by a group-sparse model with the pattern from the guided image, where the eigenvectors are chosen in the error control of the noise deviation. Moreover, a modified group orthogonal matching pursuit algorithm is developed to efficiently solve the above group sparse model. The experiments show that our method not only improves the practicality of the EGL methods with the dependence reduction of the parameter setting, but also can outperform some well-developed denoising methods, especially for noise with large deviations.

Paper Details

Date Published: 4 August 2016
PDF: 11 pages
J. Electron. Imaging. 25(4) 043019 doi: 10.1117/1.JEI.25.4.043019
Published in: Journal of Electronic Imaging Volume 25, Issue 4
Show Author Affiliations
Ying Chen, Southeast Univ. (China)
Yibin Tang, Hohai Univ. (China)
Ning Xu, Hohai Univ. (China)
Lin Zhou, Southeast Univ. (China)
Li Zhao, Southeast Univ. (China)

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