Simultaneous image fusion and denoising by using fractional-order gradient information

Jin Jin Mei, Yiqiu Dong*, Ting Zhu Huang

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Image fusion and denoising are significant in image processing because of the availability of multi-sensor and the presence of the noise. The first-order and second-order gradient information have been effectively applied to deal with fusing the noise-free source images. In this paper, we utilize the fractional-order derivatives to represent image features, and propose two new convex variational models for fusing noisy source images. Furthermore, we apply an alternating direction method of multiplier (ADMM) to solve the minimization problems in the proposed models. Numerical experiments show that the proposed methods outperform the conventional total variation methods for simultaneously fusing and denoising.

Original languageEnglish
JournalJournal of Computational and Applied Mathematics
Volume351
Pages (from-to)212-227
ISSN0377-0427
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • Alternating direction method of multiplier
  • Fractional-order derivative
  • Image fusion and denoising
  • Inverse problem
  • Structure tensor

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