CFEN-ViT-Dehazing

Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing

This repository contains PyTorch code of our paper: Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing

Test Your Datasets

1. Download the pretained models: Baidu Yun, Passward：cfen

2. Unzip them into the /checkpoints/xxx/;

3. The test images (512x512) should be put in [your test data root]/hazy/;

4. Run the following commands:

   1). Homogeneous dehazing

   (RESIDE-SOTS Dataset)：

   python test.py --dataroot [Your testing data root] --name iid_hlgvit_crs_gd4_cfs_v3_reside --n_feats 24 --hidden_dim_ratio 4 --sb --out_all --which_epoch 32

   (O-HAZE Dataset):

   python test.py --dataroot [Your testing data root] --name iid_hlgvit_crs_gd4_cfs_v3_ohaze --n_feats 24 --hidden_dim_ratio 4 --sb --out_all --which_epoch 20

   2). Non-homogeneous dehazing

   (NH-HAZE):

   python test.py --dataroot [Your testing data root] --name iid_hlgvit_crs_gd4_cfs_v3_nhhaze --n_feats 24 --hidden_dim_ratio 4 --sb --out_all --which_epoch 20

   3). Nighttime dehazing

   python test.py --dataroot [Your testing data root] --name iid_hlgvit_crs_gd4_cfs_v3_nighttime --n_feats 24 --hidden_dim_ratio 2 --sb --out_all

   4). Real_world dehazing

   python test.py --dataroot [Your testing data root] --name iid_hlgvit_crs_gd4_cfs_v3_daytime_realworld --n_feats 24 --hidden_dim_ratio 2 --sb --out_all

Results

Hazy image

Dehazing result (Ours)

Hazy image

Dehazing result (Ours)

Hazy image

Dehazing result (Ours)

Hazy image

Dehazing result (Ours)

Citation

If you find this code useful for your research, please cite the paper:

Dong Zhao, Jia Li, Hongyu Li, Long Xu, "Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing", Arxiv