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Selection of regularization parameter in GMM based image denoising method

  • Yuhui Zheng
  • Min Li
  • Jianwei Zhang
  • Jin Wang
Article

Abstract

Currently, the image denoising methods using Gaussian mixture model to learn image prior have received much attention. Among these methods, expected patch log likelihood based image denoising approach has been shown to be surprisingly competitive in image restoration. However, recent related works generally utilize global regularization parameter that influences the performance of denoising algorithm. In this paper, with the consideration that the Gaussian mixture model has the capability of clustering, we propose an adaptive estimation method of regularization parameter for expected patch log likelihood based image denoising. Our method jointly employs the Lagrange multiplier technique and entropy concept to select regularization parameter for each underlying cluster. Experimental results illustrate the relatively good performance of our image denoising method in terms of visual improvement and peak signal to noise ratio.

Keywords

Image denoising Gaussian mixture model Regularization parameter selection Lagrange multiplier method 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grants 61572257 and 61672295, the Natural Science Fund for Colleges and Universities in Jiangsu Province (15KJB520025), and the PAPD (a project funded by the priority academic program development of Jiangsu Higher Education Institutions).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer and SoftwareNanjing University of Information Science & TechnologyNanjingChina
  2. 2.College of Math and StatisticsNanjing University of Information Science & TechnologyNanjingChina
  3. 3.College of Information EngineeringYangzhou UniversityYangzhouChina

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