Variational image fusion with first and second-order gradient information

Fang Li, Tieyong ZENG

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Image fusion is important in computer vision where the main goal is to integrate several sources images of the same scene into a more informative image. In this paper, we propose a variational image fusion method based on the first and second-order gradient information. Firstly, we select the target first-order and second-order gradient information from the source images by a new and simple salience criterion. Then we build our model by requiring that the first-order and second-order gradient information of the fused image match with the target gradient information, and meanwhile the fused image is close to the source images. Theoretically, we can prove that our variational model has a unique minimizer. In the numerical implementation, we take use of the split Bregman method to get an efficient algorithm. Moreover, four-direction difference scheme is proposed to discrete gradient operator, which can dramatically enhance the fusion quality. A number of experiments and comparisons with some popular existing methods demonstrate that the proposed model is promising in various image fusion applications.

Original languageEnglish
Pages (from-to)200-222
Number of pages23
JournalJournal of Computational Mathematics
Volume34
Issue number2
DOIs
Publication statusPublished - 1 Mar 2016

Scopus Subject Areas

  • Computational Mathematics

User-Defined Keywords

  • Bounded variation
  • Feature selection
  • Image fusion
  • Second bounded variation
  • Split Bregman

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