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A fractional variational image denoising model with two-component regularization terms

Author

Listed:
  • Li, Xiao
  • Meng, Xiaoying
  • Xiong, Bo

Abstract

Image denoising is to recover true image from noisy image. Many image deonising models are proposed during the last decades. Some models preserve the margin of tissue, i.e., TV model, while the others, i.e., LLT model, prefer smooth solutions. By decomposing true image into cartoon part and texture part, we propose a fractional image denoising model with two-component regularization terms. Setting some appropriate parameters, the proposed model can deal with both smooth and non-smooth image denosing problems. The existence and uniqueness of solution for the variational model are proved. Moreover, a Split-Bregman(S-B) based numerical algorithm to solve this model is also proposed to validate the theoretical results. Numerical tests show that the proposed model can produce competitive denoising result to the other three published models.

Suggested Citation

  • Li, Xiao & Meng, Xiaoying & Xiong, Bo, 2022. "A fractional variational image denoising model with two-component regularization terms," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    • Handle: RePEc:eee:apmaco:v:427:y:2022:i:c:s0096300322002521
    DOI: 10.1016/j.amc.2022.127178
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    References listed on IDEAS

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    1. Tingting Wu, 2016. "Variable Splitting Based Method for Image Restoration with Impulse Plus Gaussian Noise," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-16, November.
    2. Shama, Mu-Ga & Huang, Ting-Zhu & Liu, Jun & Wang, Si, 2016. "A convex total generalized variation regularized model for multiplicative noise and blur removal," Applied Mathematics and Computation, Elsevier, vol. 276(C), pages 109-121.
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    Cited by:

    1. Wang, Jian & Han, Ziwei & Jiang, Wenjing & Kim, Junseok, 2023. "A fast, efficient, and explicit phase-field model for 3D mesh denoising," Applied Mathematics and Computation, Elsevier, vol. 458(C).

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