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Total generalized variation and wavelet transform for impulsive image restoration

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Abstract

Combining the advantages of total generalized variation and wavelet transform, we propose a new hybrid model based on \(L_{1}\) norm for image restoration. Numerically, we obtain the optimal solution by alternating iteration of the efficient augmented Lagrangian method. For the selection of regularization parameters, we use an adaptive criterion based on the value function. Experimental results show that the proposed algorithm can remove impulse noise well and reduce staircase effect while preserving edges. Compared with several classical methods, the proposed model has also higher PSNR and SSIM values.

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Acknowledgements

The authors would like to thank the editor and reviewers for helpful comments that improved the paper. This work was supported by the Fundamental Research Funds for the Central Universities (No. 19CX05003A-2).

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  1. College of Science, China University of Petroleum, Qingdao, 266580, People’s Republic of China

    Lingling Jiang & Haiqing Yin

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  1. Lingling Jiang
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  2. Haiqing Yin
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Correspondence to Lingling Jiang.

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Jiang, L., Yin, H. Total generalized variation and wavelet transform for impulsive image restoration. SIViP 16, 773–781 (2022). https://doi.org/10.1007/s11760-021-02017-x

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