This is the PyTorch implementation of the paper 'Robust Unpaired Image Dehazing via Density and Depth Decomposition'.

This paper presents an advanced version of our original work, termed D4+. The upgraded framework exploits a dual contrastive perceptual loss to further improve the performance of both haze removal and generation. Moreover, it successfully extends the scope of training from indoor synthetic datasets to real outdoor scenes and achieves remarkable improvement in dehazing performance. Such extension together with the introduced contrastive loss makes the whole framework more robust and effective for real-world hazy scenes. More detailed experiments on both synthetic and real datasets are conducted to validate the effectiveness of our method.

• Python 3.7
• Pytorch 1.7.1
• NVIDIA GPU + CUDA cuDNN

After downloading the dataset, please use scripts/flist.py to generate the file lists. For example, to generate the training set file list on the SOTS-indoor testset, you should run:

python scripts/flist.py --path path_to_SOTS_indoor_hazy_path --output ./datasets/sots_test_hazy_indoor.flist

Please notice that the ground truth images of SOTS-indoor have additional white border, you can crop it first.

For training on the synthetic indoor dataset, we used ITS dataset, you can follow the operations above to generate the training file lists.

python scripts/flist.py --path ITS_train_hazy_path --output ./datasets/its_train_hazy.flist
python scripts/flist.py --path ITS_train_gt_path --output ./datasets/its_train_gt.flist

For training on real outdoor scenes, we used images collected from OTS and URHI dataset. Download link

To use the pre-trained models, download it from the following link then copy it to the corresponding checkpoints folder, like ./checkpoints/quick_test

Pretrained model

To hold a quick-testing of our dehazing model, download our pre-trained model and put it into checkpoints/quick_test, then run:

python3 test.py --model 1 --checkpoints ./checkpoints/quick_test

and check the results in 'checkpoints/quick_test/results'

If you want to see the depth estimation and haze generation results, change the TEST_MODE term from hazy to clean, then run the same command.

1)Prepare the training datasets following the operations in the Dataset part. 2)Add a config file 'config.yml' in your checkpoints folder. We provide an example checkpoints folder and config file in ./checkpoints/train_example, remember to fill the correct dataset in the config file. 3)Train the model, for example:

python train.py --model 1 --checkpoints ./checkpoints/train_example

1)Prepare the testing datasets following the operations in the Dataset part. 2)Put the trained weight in the checkpoint folder 2)Add a config file 'config.yml' in your checkpoints folder. We provide an example checkpoints folder and config file in ./checkpoints/test_example, remember to fill the correct dataset in the config file. 3)Test the model, for example:

python test.py --model 1 --checkpoints ./checkpoints/test_example