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Edge coherence-weighted second-order variational model for image denoising

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Abstract

High-order variational models have the ability to remove the staircase effect generated by the total variation regularizer. They, however, tend to blur object edges. To overcome this drawback, we introduce an edge coherence-weighted second-order (ECSO) model for image denoising. We propose novel regularizers that use the edge coherence quantity to adjust the strength of regularization according to the characteristics of each pixel. We then adapt the split Bregman algorithm to solve the proposed model. All the subproblems are solved efficiently using the fast Fourier transform and the shrinkage operator. Extensive experiments show that the proposed model outperforms state-of-the-art high-order variational models for image denoising.

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Authors and Affiliations

  1. Faculty of Electronics and Telecommunication Engineering, University of Science and Technology - The University of Danang, 54 Nguyen Luong Bang, Danang, Vietnam

    Tran Dang Khoa Phan

  2. Faculty of Economics, University of Economics - The University of Danang, 71 Ngu Hanh Son, Danang, Vietnam

    Thi Hoang Yen Tran

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  1. Tran Dang Khoa Phan
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  2. Thi Hoang Yen Tran
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Correspondence to Tran Dang Khoa Phan.

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Phan, T.D.K., Tran, T.H.Y. Edge coherence-weighted second-order variational model for image denoising. SIViP 16, 2313–2320 (2022). https://doi.org/10.1007/s11760-022-02209-z

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  • DOI: https://doi.org/10.1007/s11760-022-02209-z

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