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Fast Algorithms for Poisson Image Denoising Using Fractional-Order Total Variation

  • Jun Zhang
  • Mingxi Ma
  • Chengzhi DengEmail author
  • Zhaoming Wu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 834)

Abstract

In this paper, a new Poisson image denoising model based on fractional-order total variation regularization is proposed. To obtain its global optimal solution, the augmented Lagrangian method, the Chambolle’s dual algorithm and the primal-dual algorithm are introduced. Experimental results are supplied to demonstrate the effectiveness and efficiency of the proposed algorithms for solving our proposed model, with comparison to the total variation Poisson image denoising model.

Keywords

Poisson denoising Fractional-order total variation Augmented Lagrangian method Dual algorithm Primal-dual algorithm 

Notes

Acknowledgements

The research has been supported by the Science and Technology Project of Jiangxi Provincial Department of Education (GJJ161111, GJJ171015), the NNSF of China grants (61865012), the CSC (201708360066), and the NSF of Jiangxi Province (20161BAB202040, 20151BAB207010).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jun Zhang
    • 1
    • 2
  • Mingxi Ma
    • 2
  • Chengzhi Deng
    • 1
    Email author
  • Zhaoming Wu
    • 1
  1. 1.Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent ProcessingNanchang Institute of TechnologyNanchangChina
  2. 2.College of Science, Nanchang Institute of TechnologyNanchangChina

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