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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