Infrared and visible image fusion via detail preserving adversarial learning

Ma, J; Liang, P; Yu, W; Chen, C; Guo, X; Wu, J; Jiang, J

Infrared and visible image fusion via detail preserving adversarial learning

Ma, J Liang, P Yu, W Chen, C Guo, X Wu, J Jiang, J

Permalink

Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Information Fusion, 2020, 54, pp. 85-98
Issue Date:: 2020-02-01

Closed Access

	Filename	Description	Size
	1-s2.0-S1566253519300314-main.pdf	Published version	4.46 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Ma, J
dc.contributor.author	Liang, P
dc.contributor.author	Yu, W
dc.contributor.author	Chen, C
dc.contributor.author	Guo, X
dc.contributor.author	Wu, J
dc.contributor.author	Jiang, J
dc.date.accessioned	2021-02-17T21:17:20Z
dc.date.available	2021-02-17T21:17:20Z
dc.date.issued	2020-02-01
dc.identifier.citation	Information Fusion, 2020, 54, pp. 85-98
dc.identifier.issn	1566-2535
dc.identifier.uri	http://hdl.handle.net/10453/146187
dc.description.abstract	© 2019 Elsevier B.V. TargefTablets can be detected easily from the background of infrared images due to their significantly discriminative thermal radiations, while visible images contain textural details with high spatial resolution which are beneficial to the enhancement of target recognition. Therefore, fused images with abundant detail information and effective target areas are desirable. In this paper, we propose an end-to-end model for infrared and visible image fusion based on detail preserving adversarial learning. It is able to overcome the limitations of the manual and complicated design of activity-level measurement and fusion rules in traditional fusion methods. Considering the specific information of infrared and visible images, we design two loss functions including the detail loss and target edge-enhancement loss to improve the quality of detail information and sharpen the edge of infrared targets under the framework of generative adversarial network. Our approach enables the fused image to simultaneously retain the thermal radiation with sharpening infrared target boundaries in the infrared image and the abundant textural details in the visible image. Experiments conducted on publicly available datasets demonstrate the superiority of our strategy over the state-of-the-art methods in both objective metrics and visual impressions. In particular, our results look like enhanced infrared images with clearly highlighted and edge-sharpened targets as well as abundant detail information.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Information Fusion
dc.relation.isbasedon	10.1016/j.inffus.2019.07.005
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Infrared and visible image fusion via detail preserving adversarial learning
dc.type	Journal Article
utslib.citation.volume	54
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-17T21:17:09Z
pubs.publication-status	Published
pubs.volume	54

Abstract:

© 2019 Elsevier B.V. TargefTablets can be detected easily from the background of infrared images due to their significantly discriminative thermal radiations, while visible images contain textural details with high spatial resolution which are beneficial to the enhancement of target recognition. Therefore, fused images with abundant detail information and effective target areas are desirable. In this paper, we propose an end-to-end model for infrared and visible image fusion based on detail preserving adversarial learning. It is able to overcome the limitations of the manual and complicated design of activity-level measurement and fusion rules in traditional fusion methods. Considering the specific information of infrared and visible images, we design two loss functions including the detail loss and target edge-enhancement loss to improve the quality of detail information and sharpen the edge of infrared targets under the framework of generative adversarial network. Our approach enables the fused image to simultaneously retain the thermal radiation with sharpening infrared target boundaries in the infrared image and the abundant textural details in the visible image. Experiments conducted on publicly available datasets demonstrate the superiority of our strategy over the state-of-the-art methods in both objective metrics and visual impressions. In particular, our results look like enhanced infrared images with clearly highlighted and edge-sharpened targets as well as abundant detail information.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/146187