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A Dual Adaptive Regularization Method to Remove Mixed Gaussian-Poisson Noise

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10116))

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Abstract

The noise in low photon-counting imaging system can often be described as mixed Gaussian-Poisson noise. Regularization methods are required to replace the ill-posed image denoising problems with an approximate well-posed one. However, the sole constraint in non-adaptive regularization methods is harmful to a good balance between the noise-removing and detail-preserving. Meanwhile, most existing adaptive regularization methods were aimed at unitary noise model and dual adaptive regularization scheme remained scarce. Thus, we propose a dual adaptive regularization method based on local variance to remove the mixed Gaussian-Poisson noise in micro focus X-ray images. Firstly, we raise a new 3-step image segmentation scheme based on local variance. Then, a self-adaptive p-Laplace variation function is used as the regularization operator while the regularization parameter is adaptively obtained via a barrier function. Finally, experimental results demonstrate the superiority of the proposed method in suppressing noise and preserving fine details.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grant 61403146 and the Fundamental Research Funds for the Central Universities (x2zd-D2155120).

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Authors and Affiliations

  1. School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, Guangdong, People’s Republic of China

    Ziling Wu, Hongxia Gao, Ge Ma & Yanying Wan

  2. Engineering Research Centre for Manufacturing Equipment of Ministry of Education, South China University of Technology, Guangzhou, 510641, Guangdong, People’s Republic of China

    Ziling Wu, Hongxia Gao, Ge Ma & Yanying Wan

Authors
  1. Ziling Wu
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  2. Hongxia Gao
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  3. Ge Ma
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  4. Yanying Wan
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Corresponding author

Correspondence to Ge Ma .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Wu, Z., Gao, H., Ma, G., Wan, Y. (2017). A Dual Adaptive Regularization Method to Remove Mixed Gaussian-Poisson Noise. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-54407-6_14

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