[CVPRW 2023] SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing

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SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing
Yu Guo, Yuan Gao, Ryan Wen Liu^*, Yuxu Lu, Jingxiang Qu, Shengfeng He, Wenqi Ren (* indicates corresponding author)
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops

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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Requirement

• Python 3.7
• Pytorch 1.9.1

Network Architecture

Train

• Place the training and test image pairs in the data folder.
• Run data/makedataset.py to generate the NH-Haze20-21-23.h5 file.
• Run train.py to start training.

Test

• Place the pre-training weight in the checkpoint folder.
• Place test hazy images in the input folder.
• Modify the weight name in the test.py.
  

parser.add_argument("--model_name", type=str, default='Gmodel_40', help='model name')

• Run test.py
• The results are saved in output folder.

Pre-training Weight Download

• The weight40 for the NTIRE2023 val/test datasets, i.e., the weight used in the NTIRE2023 challenge.
• The weight105 for the NTIRE2020/2021/2023 datasets.
• The weight120 for the NTIRE2020/2021/2023 datasets (Add the 15 tested images as the training dataset).

Citation

@inproceedings{guo2023scanet,
  title={SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing},
  author={Guo, Yu and Gao, Yuan and Liu, Wen and Lu, Yuxu and Qu, Jingxiang and He, Shengfeng and Ren, Wenqi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={1884--1893},
  month={June},
  year={2023}
}

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