Pyramid Global Context Network for Image Dehazing

• Published in: IEEE transactions on circuits and systems for video technology Vol. 31; no. 8; pp. 3037 - 3050
• Main Authors: Zhao, Dong, Xu, Long, Ma, Lin, Li, Jia, Yan, Yihua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-08-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Atmospheric modeling; Atmospheric models; Atmospheric scattering; Context; Context modeling; Convolution; deep learning; dilated residual U-Net (DRU); Estimation; global context modeling; Haze; Image dehazing; pyramid global context (PGC); Scattering; Solid modeling; Visibility
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2020.3036992

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.