Variational image fusion approach based on TGV and local information. Issue 4 (16th February 2018)

• Record Type: Journal Article
• Authors: Wang, Qinxia; Yang, Xiaoping
• Is Part Of: IET computer vision. Volume 12:Issue 4(2018)
• Page Start: 535
• Page End: 541
• Publication Date: 2018-02-16
• Subjects: image fusion -- feature extraction -- gradient methods -- medical image processing -- computerised tomography -- biomedical MRI; variational image fusion approach -- total generalised variation -- local gradient information -- computed tomography -- medical images -- magnetic resonance -- second-order TGV -- image feature extraction -- primal-dual algorithm; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2017.0451
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : In this study, the authors propose a variational approach based on total generalised variation (TGV) and local gradient information to fuse multi‐focus images as well as medical images of computed tomography and magnetic resonance. They use the second‐order TGV as the regularisation term and local gradient information as the fusion weight to extract image features. To compute the new model effectively, the primal‐dual algorithm is carried out. Various experiments are made to verify the effectiveness of the proposed methods.