Nonconvex-TV Based Image Restoration with Impulse Noise Removal

Xiongjun Zhang, Minru Bai, Michael K. Ng*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

52 Citations (Scopus)
30 Downloads (Pure)


In this paper, we propose and study a nonconvex data fitting term and a total variation regularization term for image restoration with impulse noise removal. The proposed model is different from existing image restoration models where the data fitting term is based on the ℓ1- or ℓ2- norm, and the regularization term is based on the total variation, ℓ1-norm, or some nonconvex functions. Theoretically, we analyze the properties of minimizers of the proposed objective function with the nonconvex data fitting term and the total variation regularization term. We show that minimizers can preserve piecewise constant regions or match with the data points perfectly. This property is particularly useful for impulse noise removal. The proposed image restoration model can be solved by the proximal linearized minimization algorithm, and the global convergence of the iterative algorithm can also be established according to Kurdyka-Łojasiewicz property. The performance of the proposed model is tested for image restoration with salt-and-pepper impulse noise or random-valued impulse noise. We demonstrate that the restored images by the proposed Nonconvex-TV model are better (in terms of PSNR and visual quality) than those by the other existing data fitting plus regularization models, including ℓ1 plus total variation (L1TV) and ℓ1 plus nonconvex (L1Nonconvex) methods.

Original languageEnglish
Pages (from-to)1627-1667
Number of pages41
JournalSIAM Journal on Imaging Sciences
Issue number3
Publication statusPublished - 28 Sept 2017

Scopus Subject Areas

  • Mathematics(all)
  • Applied Mathematics

User-Defined Keywords

  • Kurdyka-Łojasiewicz property
  • Nonconvex data fitting
  • Nonsmooth optimization
  • Proximal linearized minimization
  • Total variation regularization


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