Fast filtering image fusion

Zhan, K; Xie, Y; Wang, H; Min, Y

Fast filtering image fusion

Zhan, K

Xie, Y Wang, H Min, Y

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Publication Type:: Journal Article
Citation:: Journal of Electronic Imaging, 2017, 26 (6)
Issue Date:: 2017-11-01

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Field	Value	Language
dc.contributor.author	Zhan, K https://orcid.org/0000-0002-8000-5682	en_US
dc.contributor.author	Xie, Y	en_US
dc.contributor.author	Wang, H	en_US
dc.contributor.author	Min, Y	en_US
dc.date.issued	2017-11-01	en_US
dc.identifier.citation	Journal of Electronic Imaging, 2017, 26 (6)	en_US
dc.identifier.issn	1017-9909	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126138
dc.description.abstract	© 2017 SPIE and IS & T. Image fusion aims at exploiting complementary information in multimodal images to create a single composite image with extended information content. An image fusion framework is proposed for different types of multimodal images with fast filtering in the spatial domain. First, image gradient magnitude is used to detect contrast and image sharpness. Second, a fast morphological closing operation is performed on image gradient magnitude to bridge gaps and fill holes. Third, the weight map is obtained from the multimodal image gradient magnitude and is filtered by a fast structure-preserving filter. Finally, the fused image is composed by using a weighed-sum rule. Experimental results on several groups of images show that the proposed fast fusion method has a better performance than the state-of-the-art methods, running up to four times faster than the fastest baseline algorithm.	en_US
dc.relation.ispartof	Journal of Electronic Imaging	en_US
dc.relation.isbasedon	10.1117/1.JEI.26.6.063004	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Fast filtering image fusion	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	26	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2017 SPIE and IS & T. Image fusion aims at exploiting complementary information in multimodal images to create a single composite image with extended information content. An image fusion framework is proposed for different types of multimodal images with fast filtering in the spatial domain. First, image gradient magnitude is used to detect contrast and image sharpness. Second, a fast morphological closing operation is performed on image gradient magnitude to bridge gaps and fill holes. Third, the weight map is obtained from the multimodal image gradient magnitude and is filtered by a fast structure-preserving filter. Finally, the fused image is composed by using a weighed-sum rule. Experimental results on several groups of images show that the proposed fast fusion method has a better performance than the state-of-the-art methods, running up to four times faster than the fastest baseline algorithm.

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http://hdl.handle.net/10453/126138