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Image denoising via adaptive eigenvectors of graph Laplacian

[+] Author Affiliations
Ying Chen, Lin Zhou, Li Zhao

Southeast University, School of Information Science and Engineering, No. 2 Sipailou, Nanjing 210096, China

Yibin Tang, Ning Xu

Hohai University, College of Internet of Things Engineering, No. 200 Jinling North Road, Changzhou 213022, China

J. Electron. Imaging. 25(4), 043019 (Aug 04, 2016). doi:10.1117/1.JEI.25.4.043019
History: Received March 25, 2016; Accepted July 14, 2016
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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.

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Citation

Ying Chen ; Yibin Tang ; Ning Xu ; Lin Zhou and Li Zhao
"Image denoising via adaptive eigenvectors of graph Laplacian", J. Electron. Imaging. 25(4), 043019 (Aug 04, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.4.043019


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