SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous
Image Dehazing
- URL: http://arxiv.org/abs/2304.08444v1
- Date: Mon, 17 Apr 2023 17:05:29 GMT
- Title: SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous
Image Dehazing
- Authors: Yu Guo, Yuan Gao, Ryan Wen Liu, Yuxu Lu, Jingxiang Qu, Shengfeng He,
Wenqi Ren
- Abstract summary: Existing homogeneous dehazing methods struggle to handle the non-uniform distribution of haze in a robust manner.
We propose a novel self-paced semi-curricular attention network, called SCANet, for non-homogeneous image dehazing.
Our approach consists of an attention generator network and a scene reconstruction network.
- Score: 56.900964135228435
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The presence of non-homogeneous haze can cause scene blurring, color
distortion, low contrast, and other degradations that obscure texture details.
Existing homogeneous dehazing methods struggle to handle the non-uniform
distribution of haze in a robust manner. The crucial challenge of
non-homogeneous dehazing is to effectively extract the non-uniform distribution
features and reconstruct the details of hazy areas with high quality. In this
paper, we propose a novel self-paced semi-curricular attention network, called
SCANet, for non-homogeneous image dehazing that focuses on enhancing
haze-occluded regions. Our approach consists of an attention generator network
and a scene reconstruction network. We use the luminance differences of images
to restrict the attention map and introduce a self-paced semi-curricular
learning strategy to reduce learning ambiguity in the early stages of training.
Extensive quantitative and qualitative experiments demonstrate that our SCANet
outperforms many state-of-the-art methods. The code is publicly available at
https://github.com/gy65896/SCANet.
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