| Field |
Value |
Language |
|
dc.contributor.author |
Sun, Z |
|
|
dc.contributor.author |
Zhang, Y |
|
|
dc.contributor.author |
Bao, F |
|
|
dc.contributor.author |
Wang, P |
|
|
dc.contributor.author |
Yao, X
https://orcid.org/0000-0002-7475-0512
|
|
|
dc.contributor.author |
Zhang, C |
|
|
dc.date.accessioned |
2023-07-09T02:06:24Z |
|
|
dc.date.available |
2023-07-09T02:06:24Z |
|
|
dc.date.issued |
2022-05-31 |
|
|
dc.identifier.citation |
ACM Transactions on Multimedia Computing Communications and Applications, 2022, 18, (2), pp. 1-23 |
|
|
dc.identifier.issn |
1551-6857 |
|
|
dc.identifier.issn |
1551-6865 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/171357
|
|
|
dc.description.abstract |
<jats:p>Many real-life tasks such as military reconnaissance and traffic monitoring require high-quality images. However, images acquired in foggy or hazy weather pose obstacles to the implementation of these real-life tasks; consequently, image dehazing is an important research problem. To meet the requirements of practical applications, a single image dehazing algorithm has to be able to effectively process real-world hazy images with high computational efficiency. In this article, we present a fast and robust semi-supervised dehazing algorithm named SADnet for practical applications. SADnet utilizes both synthetic datasets and natural hazy images for training, so it has good generalizability for real-world hazy images. Furthermore, considering the uneven distribution of haze in the atmospheric environment, a Channel-Spatial Self-Attention (CSSA) mechanism is presented to enhance the representational power of the proposed SADnet. Extensive experimental results demonstrate that the presented approach achieves good dehazing performances and competitive running times compared with other state-of-the-art image dehazing algorithms.</jats:p> |
|
|
dc.language |
en |
|
|
dc.publisher |
Association for Computing Machinery (ACM) |
|
|
dc.relation.ispartof |
ACM Transactions on Multimedia Computing Communications and Applications |
|
|
dc.relation.isbasedon |
10.1145/3478457 |
|
|
dc.rights |
info:eu-repo/semantics/closedAccess |
|
|
dc.subject |
0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems |
|
|
dc.subject.classification |
Artificial Intelligence & Image Processing |
|
|
dc.subject.classification |
4603 Computer vision and multimedia computation |
|
|
dc.subject.classification |
4606 Distributed computing and systems software |
|
|
dc.subject.classification |
4607 Graphics, augmented reality and games |
|
|
dc.title |
SADnet: Semi-supervised Single Image Dehazing Method Based on an Attention Mechanism |
|
|
dc.type |
Journal Article |
|
|
utslib.citation.volume |
18 |
|
|
utslib.for |
0803 Computer Software |
|
|
utslib.for |
0805 Distributed Computing |
|
|
utslib.for |
0806 Information Systems |
|
|
pubs.organisational-group |
/University of Technology Sydney |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering |
|
|
utslib.copyright.status |
closed_access |
* |
|
dc.date.updated |
2023-07-09T02:06:09Z |
|
|
pubs.issue |
2 |
|
|
pubs.publication-status |
Published |
|
|
pubs.volume |
18 |
|
|
utslib.citation.issue |
2 |
|
