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Fractional-Order Variational Image Fusion and Denoising Based on Data-Driven Tight Frame

Author

Listed:
  • Ru Zhao

    (School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China)

  • Jingjing Liu

    (School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China)

Abstract

Multi-modal image fusion can provide more image information, which improves the image quality for subsequent image processing tasks. Because the images acquired using photon counting devices always suffer from Poisson noise, this paper proposes a new three-step method based on the fractional-order variational method and data-driven tight frame to solve the problem of multi-modal image fusion for images corrupted by Poisson noise. Thus, this article obtains fused high-quality images while removing Poisson noise. The proposed image fusion model can be solved by the split Bregman algorithm which has significant stability and fast convergence. The numerical results on various modal images show the excellent performance of the proposed three-step method in terms of numerical evaluation metrics and visual quality. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on image fusion with Poisson noise.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2260-:d:1145003
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    References listed on IDEAS

    as
    1. Manuel Henriques & Duarte Valério & Paulo Gordo & Rui Melicio, 2021. "Fractional-Order Colour Image Processing," Mathematics, MDPI, vol. 9(5), pages 1-15, February.
    2. 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.
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    Cited by:

    1. Ming Lv & Zhenhong Jia & Liangliang Li & Hongbing Ma, 2023. "Multi-Focus Image Fusion via PAPCNN and Fractal Dimension in NSST Domain," Mathematics, MDPI, vol. 11(18), pages 1-23, September.

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