Progressive Stereo Image Dehazing Network via Cross-View Region Interaction

Wang, J; Wei, Y; Zhang, Z; Fan, J; Zhao, Y; Yang, Y; Wang, M

Progressive Stereo Image Dehazing Network via Cross-View Region Interaction

Wang, J Wei, Y Zhang, Z Fan, J Zhao, Y Yang, Y

Wang, M

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Multimedia, 2024, 26, pp. 7490-7502
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Wang, J
dc.contributor.author	Wei, Y
dc.contributor.author	Zhang, Z
dc.contributor.author	Fan, J
dc.contributor.author	Zhao, Y
dc.contributor.author	Yang, Y https://orcid.org/0000-0002-0512-880X
dc.contributor.author	Wang, M
dc.date.accessioned	2024-08-07T04:56:21Z
dc.date.available	2024-08-07T04:56:21Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Multimedia, 2024, 26, pp. 7490-7502
dc.identifier.issn	1520-9210
dc.identifier.issn	1941-0077
dc.identifier.uri	http://hdl.handle.net/10453/180250
dc.description.abstract	Stereo image dehazing aims to restore haze-free images by leveraging the complementary information contained in binocular images. Current methods primarily focus on designing image-level modules and pipelines to utilize complementary information between the left and right-view images. However, these image-level cross-view interactions overlook regional differences in haze concentration and stereo image disparity maps. Consequently, we propose a Progressive Stereo Image Dehazing Network via Cross-view Region Interaction, termed PSIDNet, which fully considers the internal characteristics and external manifestation of haze and disparity, and explicitly addresses the stereo image dehazing task by a regional-aware interactive mechanism. Specifically, we divide hazy images into regions and independently interact with left and right-view information at region levels, meaning weights are not shared across regional patches. This approach allows us to treat different regions with different priorities, i.e., concentrate on regional patches with heavier haze concentration and larger disparities, hence enabling more accurate restoration of hazy images. Furthermore, we introduce an effective cross-view region interactive block that extracts information based on the channel dimension of dual views and later adopts matrix multiplication to generate mutual attention maps based on the fused features. Extensive experiments on synthetic and real-scenario datasets demonstrate the efficacy of our method, compared to other related monocular and stereo image dehazing and restoration methods.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Multimedia
dc.relation.isbasedon	10.1109/TMM.2024.3368918
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Progressive Stereo Image Dehazing Network via Cross-View Region Interaction
dc.type	Journal Article
utslib.citation.volume	26
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T04:56:13Z
pubs.publication-status	Published
pubs.volume	26

Abstract:

Stereo image dehazing aims to restore haze-free images by leveraging the complementary information contained in binocular images. Current methods primarily focus on designing image-level modules and pipelines to utilize complementary information between the left and right-view images. However, these image-level cross-view interactions overlook regional differences in haze concentration and stereo image disparity maps. Consequently, we propose a Progressive Stereo Image Dehazing Network via Cross-view Region Interaction, termed PSIDNet, which fully considers the internal characteristics and external manifestation of haze and disparity, and explicitly addresses the stereo image dehazing task by a regional-aware interactive mechanism. Specifically, we divide hazy images into regions and independently interact with left and right-view information at region levels, meaning weights are not shared across regional patches. This approach allows us to treat different regions with different priorities, i.e., concentrate on regional patches with heavier haze concentration and larger disparities, hence enabling more accurate restoration of hazy images. Furthermore, we introduce an effective cross-view region interactive block that extracts information based on the channel dimension of dual views and later adopts matrix multiplication to generate mutual attention maps based on the fused features. Extensive experiments on synthetic and real-scenario datasets demonstrate the efficacy of our method, compared to other related monocular and stereo image dehazing and restoration methods.

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http://hdl.handle.net/10453/180250