Benchmarking Single-Image Dehazing and beyond

Li, B; Ren, W; Fu, D; Tao, D; Feng, D; Zeng, W; Wang, Z

Benchmarking Single-Image Dehazing and beyond

Li, B Ren, W Fu, D Tao, D

Feng, D Zeng, W Wang, Z

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2019, 28 (1), pp. 492 - 505
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Li, B	en_US
dc.contributor.author	Ren, W	en_US
dc.contributor.author	Fu, D	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Feng, D	en_US
dc.contributor.author	Zeng, W	en_US
dc.contributor.author	Wang, Z	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2019, 28 (1), pp. 492 - 505	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138296
dc.description.abstract	© 1992-2012 IEEE. We present a comprehensive study and evaluation of existing single-image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single-Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics to no-reference metrics and to subjective evaluation, and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of the state-of-the-art dehazing algorithms, and suggest promising future directions.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2018.2867951	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Benchmarking Single-Image Dehazing and beyond	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	28	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	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	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	28	en_US

Abstract:

© 1992-2012 IEEE. We present a comprehensive study and evaluation of existing single-image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single-Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics to no-reference metrics and to subjective evaluation, and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of the state-of-the-art dehazing algorithms, and suggest promising future directions.

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http://hdl.handle.net/10453/138296