FFA-Net: Feature Fusion Attention Network for Single Image Dehazing Article Swipe
Qin Xu
,
Zhilin Wang
,
Yuanchao Bai
,
Xiaodong Xie
,
Huizhu Jia
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v34i07.6865
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v34i07.6865
In this paper, we propose an end-to-end feature fusion at-tention network (FFA-Net) to directly restore the haze-free image. The FFA-Net architecture consists of three key components:1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels. FA treats different features and pixels unequally, which provides additional flexibility in dealing with different types of information, expanding the representational ability of CNNs. 2) A basic block structure consists of Local Residual Learning and Feature Attention, Local Residual Learning allowing the less important information such as thin haze region or low-frequency to be bypassed through multiple local residual connections, let main network architecture focus on more effective information. 3) An Attention-based different levels Feature Fusion (FFA) structure, the feature weights are adaptively learned from the Feature Attention (FA) module, giving more weight to important features. This structure can also retain the information of shallow layers and pass it into deep layers.The experimental results demonstrate that our proposed FFA-Net surpasses previous state-of-the-art single image dehazing methods by a very large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 30.23 dB to 36.39 dB on the SOTS indoor test dataset. Code has been made available at GitHub.
Related Topics
Concepts
Feature (linguistics)
Residual
Computer science
Artificial intelligence
Pixel
Margin (machine learning)
Haze
Net (polyhedron)
Block (permutation group theory)
Pattern recognition (psychology)
Boosting (machine learning)
Feature learning
Channel (broadcasting)
Image (mathematics)
Computer vision
Algorithm
Mathematics
Machine learning
Telecommunications
Geometry
Physics
Meteorology
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v34i07.6865
- https://ojs.aaai.org/index.php/AAAI/article/download/6865/6719
- OA Status
- diamond
- Cited By
- 1458
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2998249728
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2998249728Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1609/aaai.v34i07.6865Digital Object Identifier
- Title
-
FFA-Net: Feature Fusion Attention Network for Single Image DehazingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-03Full publication date if available
- Authors
-
Qin Xu, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, Huizhu JiaList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v34i07.6865Publisher landing page
- PDF URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/6865/6719Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/6865/6719Direct OA link when available
- Concepts
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Feature (linguistics), Residual, Computer science, Artificial intelligence, Pixel, Margin (machine learning), Haze, Net (polyhedron), Block (permutation group theory), Pattern recognition (psychology), Boosting (machine learning), Feature learning, Channel (broadcasting), Image (mathematics), Computer vision, Algorithm, Mathematics, Machine learning, Telecommunications, Geometry, Physics, Meteorology, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1458Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 307, 2024: 409, 2023: 338, 2022: 232, 2021: 130Per-year citation counts (last 5 years)
- References (count)
-
46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2020-04-03 |
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