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

  • Ziling Wu
  • Hongxia Gao
  • Ge MaEmail author
  • Yanying Wan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)


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.



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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ziling Wu
    • 1
    • 2
  • Hongxia Gao
    • 1
    • 2
  • Ge Ma
    • 1
    • 2
    Email author
  • Yanying Wan
    • 1
    • 2
  1. 1.School of Automation Science and EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China
  2. 2.Engineering Research Centre for Manufacturing Equipment of Ministry of EducationSouth China University of TechnologyGuangzhouPeople’s Republic of China

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