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Regularization Parameter Selection for Gaussian Mixture Model Based Image Denoising Method

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Book cover Advances in Computer Science and Ubiquitous Computing (UCAWSN 2016, CUTE 2016, CSA 2016)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 421))

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Abstract

Regularization parameter selection for image denoising has always been a hot issue. In this paper, an adaptive regularization parameter selection method is exploited for the Gaussian Mixture Model (GMM) based image restoration by combining the gradient matching and the local entropy of the image, which varies with different regions of the image and has a good robustness to noise. Experiment results demonstrate that our proposed adaptive regularization parameter for GMM based image restoration method performs comparatively well, both in visual effects and quantitative evaluations.

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References

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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

    J. W. Zhang & J. Liu

  2. CICAEET, College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Y. H. Zheng & J. Wang

Authors
  1. J. W. Zhang
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  2. J. Liu
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  3. Y. H. Zheng
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  4. J. Wang
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Corresponding author

Correspondence to J. W. Zhang .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea (Republic of)

    James J. (Jong Hyuk) Park

  2. Department of Computer Science, Georgia State University, Atlanta, Georgia, USA

    Yi Pan

  3. Computer Science and Engineering, Gangneung-Wonju National University, Wonju, Korea (Republic of)

    Gangman Yi

  4. University Salerno, Fisciano, Italy

    Vincenzo Loia

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© 2017 Springer Nature Singapore Pte Ltd.

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Zhang, J.W., Liu, J., Zheng, Y.H., Wang, J. (2017). Regularization Parameter Selection for Gaussian Mixture Model Based Image Denoising Method. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_47

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  • DOI: https://doi.org/10.1007/978-981-10-3023-9_47

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3022-2

  • Online ISBN: 978-981-10-3023-9

  • eBook Packages: EngineeringEngineering (R0)

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