Impulse Noise Image Restoration Using Nonconvex Variational Model and Difference of Convex Functions Algorithm

Benxin ZHANG, Guopu ZHU, Zhibin ZHU, Hongli ZHANG, Yicong ZHOU, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)


In this article, the problem of impulse noise image restoration is investigated. A typical way to eliminate impulse noise is to use an L1 norm data fitting term and a total variation (TV) regularization. However, a convex optimization method designed in this way always yields staircase artifacts. In addition, the L1 norm fitting term tends to penalize corrupted and noise-free data equally, and is not robust to impulse noise. In order to seek a solution of high recovery quality, we propose a new variational model that integrates the nonconvex data fitting term and the nonconvex TV regularization. The usage of the nonconvex TV regularizer helps to eliminate the staircase artifacts. Moreover, the nonconvex fidelity term can detect impulse noise effectively in the way that it is enforced when the observed data is slightly corrupted, while is less enforced for the severely corrupted pixels. A novel difference of convex functions algorithm is also developed to solve the variational model. Using the variational method, we prove that the sequence generated by the proposed algorithm converges to a stationary point of the nonconvex objective function. Experimental results show that our proposed algorithm is efficient and compares favorably with state-of-the-art methods.
Original languageEnglish
JournalIEEE Transactions on Cybernetics
Publication statusE-pub ahead of print - 15 Dec 2022
Externally publishedYes


  • Convex functions
  • Data models
  • Difference of convex functions algorithm (DCA)
  • Electronic mail
  • Image edge detection
  • image restoration
  • Image restoration
  • impulse noise
  • Mathematical models
  • nonconvex optimization model
  • TV


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