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Gaussian mixture model learning based image denoising method with adaptive regularization parameters

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Abstract

Gaussian mixture model learning based image denoising as a kind of structured sparse representation method has received much attention in recent years. In this paper, for further enhancing the denoised performance, we attempt to incorporate the gradient fidelity term with the Gaussian mixture model learning based image denoising method to preserve more fine structures of images. Moreover, we construct an adaptive regularization parameter selection scheme by combing the image gradient with the local entropy of the image. Experiment results show that our proposed method performs an improvement both in visual effects and peak signal to noise values.

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Acknowledgments

This work was supported in part by the NSFC(Grants 61402234 and 61402235) and the PAPD.

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Authors and Affiliations

  1. College of Math and Statistic, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jianwei Zhang & Jing Liu

  2. School of Engineering Science, University of Chinese Academy of Sciences, Beijing, 100049, China

    Tong Li

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yuhui Zheng

  4. College of Information Engineering, Yangzhou University, Yangzhou, 215127, China

    Jin Wang

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  1. Jianwei Zhang
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  2. Jing Liu
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  3. Tong Li
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  4. Yuhui Zheng
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  5. Jin Wang
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Correspondence to Jianwei Zhang.

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Zhang, J., Liu, J., Li, T. et al. Gaussian mixture model learning based image denoising method with adaptive regularization parameters. Multimed Tools Appl 76, 11471–11483 (2017). https://doi.org/10.1007/s11042-016-4214-4

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  • DOI: https://doi.org/10.1007/s11042-016-4214-4

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