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Pyramid Global Context Network for Image Dehazing.
- Source :
- IEEE Transactions on Circuits & Systems for Video Technology; Aug2021, Vol. 31 Issue 8, p3037-3050, 14p
- Publication Year :
- 2021
-
Abstract
- Haze caused by atmospheric scattering and absorption would severely affect scene visibility of an image. Thus, image dehazing for haze removal has been widely studied in the literature. Within a hazy image, haze is not confined in a small local patch/position, while widely diffusing in a whole image. Under this circumstance, global context is a crucial factor in the success of dehazing, which was seldom investigated in existing dehazing algorithms. In the literature, the global context (GC) block has been designed to learn point-wise long-range dependencies of an image for global context modeling; however, patch-wise long-range dependencies were ignored. To image dehazing, patch-wise long-range dependencies should be highlighted to cooperate with patch-wise operations of image dehazing. In this paper, we first extend the point-wise GC into a Pyramid Global Context (PGC), which is a multi-scale GC, after undergoing the pyramid pooling. Thus, patch-wise long-range dependencies can be explored by the PGC. Then, the proposed PGC is plugged into a U-Net, getting an attentive U-Net. Further, the attentive U-Net is optimized by importing ResNet’s shortcut connection and dilated convolution. Thus, the finalized dehazing model can explore both long-range and patch-wise context dependencies for global context modeling, which is crucial for image dehazing. The extensive experiments on synthetic databases and real-world hazy images demonstrate the superiority of our model over other representative state-of-the-art models from both quantitative and qualitative comparisons. [ABSTRACT FROM AUTHOR]
- Subjects :
- SOLAR radiation
DEEP learning
HAZE
ATMOSPHERIC models
PYRAMIDS
Subjects
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 31
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 153127930
- Full Text :
- https://doi.org/10.1109/TCSVT.2020.3036992