Image restoration via the adaptive TVp regularization

Zhi-Feng Pang, Ge Meng, Hui Li, Ke Chen

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
19 Downloads (Pure)


To keep structures in the restoration problem is very important via coupling the local information of the image with the proposed model. In this paper we propose a local self-adaptive ℓp -regularization model for p ∈ (0, 2) based on the total variation scheme, where the choice of p depends on the local structures described by the eigenvalues of the structure tensor. Since the proposed model as the classic ℓp problem unifies two classes of optimization problems such as the nonconvex and nonsmooth problem when p ∈ (0, 1), and the convex and smooth problem when p ∈ (1, 2), it is generally challenging to find a ready algorithmic framework to solve it. Here we propose a new and robust numerical method via coupling with the half-quadratic scheme and the alternating direction method of multipliers (ADMM). The convergence of the proposed algorithm is established and the numerical experiments illustrate the possible advantages of the proposed model and numerical methods over some existing variational-based models and methods.
Original languageEnglish
Pages (from-to)569-587
Number of pages19
JournalComputers and Mathematics with Applications
Issue number5
Early online date27 May 2020
Publication statusPublished - 1 Sept 2020


  • alternating direction method of multipliers (ADMM)
  • half quadratic scheme
  • image restoration
  • proximal point scheme
  • structure tensor


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