Learning non-negativity constrained variation for image denoising and deblurring

Tengda Wei, Linshan Wang, Ping Lin, Jialing Chen, Yangfan Wang (Lead / Corresponding author), Haiyong Zheng

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
226 Downloads (Pure)


This paper presents a heuristic Learning-based Non-Negativity Constrained Variation (L-NNCV) aiming to search the coefficients of variational model automatically and make the variation adapt different images and problems by supervised-learning strategy. The model includes two terms: a problem-based term that is derived from the prior knowledge, and an image-driven regularization which is learned by some training samples. The model can be solved by classical o-constraint method. Experimental results show that: the experimental effectiveness of each term in the regularization accords with the corresponding theoretical proof; the proposed method outperforms other PDE-based methods on image denoising and deblurring.

Original languageEnglish
Pages (from-to)852-871
Number of pages20
JournalNumerical Mathematics: Theory, Methods and Applications
Issue number4
Early online date12 Sept 2017
Publication statusPublished - 1 Nov 2017


  • Constraint
  • Image restoration
  • Learning idea
  • O-constraint method
  • TV-based model

ASJC Scopus subject areas

  • Modelling and Simulation
  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics


Dive into the research topics of 'Learning non-negativity constrained variation for image denoising and deblurring'. Together they form a unique fingerprint.

Cite this