Data and codes for "SCANet: Self-paced semi-curricular attention network for non-homogeneous image dehazing"

This record contains the data and codes for the paper "SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing" published in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
RequirementPython 3.7Pytorch 1.9.1Network ArchitectureTrainPlace 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.TestPlace 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.pyThe results is saved in output folder.Pre-training Weight DownloadThe weight40 Gmodel_40.tar for the NTIRE2023 val/test datasets, i.e., the weight used in the NTIRE2023 challenge.The weight105 Gmodel_105.tar for the NTIRE2020/2021/2023 datasets.The weight120 Gmodel_120.tar for the NTIRE2020/2021/2023 datasets (Add the 15 tested images as the training dataset).