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 journalArticle

3 Citations (Scopus)
154 Downloads (Pure)

Abstract

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
Volume10
Issue number4
Early online date12 Sep 2017
DOIs
Publication statusPublished - 1 Nov 2017

Keywords

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

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

  • Cite this