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[arXiv]
[bibtex]@InProceedings{Guo_2023_CVPR, author = {Guo, Yu and Gao, Yuan and Liu, Wen and Lu, Yuxu and Qu, Jingxiang and He, Shengfeng and Ren, Wenqi}, title = {SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {1885-1894} }
SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing
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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