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Wavelet denoising with generalized bivariate prior model

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Abstract

This paper presents a novel wavelet method based on a prior model of wavelet coefficient with a generalized bivariate distribution function. The generalized bivariate prior model extends the isotropic bivariate prior model and anisotropic prior model, and can well characterize the joint parent-child statistical properties of many categories of images. Owing to the fact that the variance of wavelet coefficients are quite different for different scales and positions, we propose a local adaptive marginal variance estimation method based on this newly generalized prior model to improve the accuracy of marginal variance by considering the distribution of wavelet coefficients as the combination of Gaussian distribution and Laplacian distribution. By simulation experiments from three aspects, the results of the proposed method outperform the mainstream isotropic wavelet methods and anisotropic wavelet methods, and the practicality of the proposed method is verified by real noise image experiments.

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Acknowledgements

The authors would like to thank the editors and anonymous reviewers for their constrctive and valuable comments, which helped in improving the presentation of our work. This work was supported by the National Natural Science Foundation of China (61403081, U1333202), and the Natural Science Foundation of Jiangsu Province (BK20140638).

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Authors and Affiliations

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People’s Republic of China

    Weili Zeng

  2. Department of Computer Science, University of Otago, Dunedin, 9016, New Zealand

    Xiping Fu

  3. Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing, 210096, People’s Republic of China

    Changhui Hu & Yijun Du

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  1. Weili Zeng
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  2. Xiping Fu
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  3. Changhui Hu
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  4. Yijun Du
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Correspondence to Weili Zeng.

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Zeng, W., Fu, X., Hu, C. et al. Wavelet denoising with generalized bivariate prior model. Multimed Tools Appl 77, 20863–20887 (2018). https://doi.org/10.1007/s11042-017-5497-9

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  • DOI: https://doi.org/10.1007/s11042-017-5497-9

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