Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt
This is the official PyTorch implementation of our dehazing method based on the FFC and ConvNeXt.
Winner award (1st place solution) of NTIRE 2023 HR NonHomogeneous Dehazing Challenge (CVPR Workshop 2023).
See more details in [Challenge Report], [Paper], [Certificate].
CUDA Version: 11.0
Python 3.8
torch==1.10.0
torchvision==0.9.0
NVIDIA GPU and CUDA
pytorch_lightning=2.0.0
timm=0.6.12
Download the pretrained ConvNext model and place it into the folder ./weights.
Download our saved model for NTIRE 2023 HR Nonhomogeneous Test set and place it into the folder ./weights to reproduce our test result.
Download our saved model for NTIRE 2023 HR Nonhomogeneous Validation set and place it into the folder ./weights to reproduce our validation result.
These weights are the checkpoints that perform best for NTIRE 23 dehazing challenge ofiicial validation set and test set.
Download above pretrained and saved models
Prepare NTIRE2023 HR Nongomogeneous Dehazing Chanllenge Validation set and Test set
Run test.py and find results in the folder ./test_result. Please check the test hazy image path (test.py line 12).
Datasets can be found below:
Reside, NH-HAZE, NH-HAZE2, HD-NH-HAZE, and our Combined Dataset.
If you want to train with your data, you can use the train.py in DW-GAN, as we adopt similar training stratety with DWGAN.
We are sorry that we didn't name our model in our paper, but we are glad you can use DWT-FFC to represent our method if you want to compare with our model.
We thank the authors of DW-GAN, LaMa, and ConvNeXt. Part of our code is built on their models.
If you find this repository helps, please consider citing:
@InProceedings{DWT-FFC_2023_CVPRW,
author = {Zhou, Han and Dong, Wei and Liu, Yangyi and Chen, Jun},
title = {Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
month = {June},
year = {2023},
pages = {1894-1903}
}