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High-Order Total Bounded Variation Model and Its Fast Algorithm for Poissonian Image Restoration

Author

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  • Jun Zhang
  • Mingxi Ma
  • Zhaoming Wu
  • Chengzhi Deng

Abstract

In this paper, a new model for image restoration under Poisson noise based on a high-order total bounded variation is proposed. Existence and uniqueness of its solution are proved. To find the global optimal solution of our strongly convex model, a split Bregman algorithm is introduced. Furthermore, a rigorous convergence theory of the proposed algorithm is established. Experimental results are provided to demonstrate the effectiveness and efficiency of the proposed method over the classic total bounded variation-based model.

Suggested Citation

  • Jun Zhang & Mingxi Ma & Zhaoming Wu & Chengzhi Deng, 2019. "High-Order Total Bounded Variation Model and Its Fast Algorithm for Poissonian Image Restoration," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-11, February.
    • Handle: RePEc:hin:jnlmpe:2502731
    DOI: 10.1155/2019/2502731
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    Cited by:

    1. Ru Zhao & Jingjing Liu, 2023. "Fractional-Order Variational Image Fusion and Denoising Based on Data-Driven Tight Frame," Mathematics, MDPI, vol. 11(10), pages 1-16, May.

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