Accepted in ACM MM'24, Sydney, Australia

HazeSpace2M: A Dataset for Haze Aware Single Image Dehazing [Download]

Md Tanvir Islam ¹, Nasir Rahim ¹, Saeed Anwar ², Muhammad Saqib ³, Sambit Bakshi ⁴, Khan Muhammad ^{1, *}

| 1. Sungkyunkwan University, South Korea | 2. KFUPM, KSA | 3. UTS, Australia | 4. NIT Rourkela, India |

| *Corresponding Author |

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HazeSpace2M Dataset

Haze Aware Dehazing

Dependencies

pip install -r requirements.txt

Dataset Download

We are preparing the complete dataset formatting with a structural naming convention. We will upload the full dataset as soon as we complete preparing the dataset with correct naming format as we displayed in the last images in this page.

Pretrained Weights

We will upload the pre-trained weights very soon.

Testing

python inference.py --gt_folder <path_to_gt> --hazy_folder <path_to_hazy> --output_dir <output_dir> --classifier <path_to_classifier> --cloudSD <path_to_cloudSD> --ehSD <path_to_ehSD> --fogSD <path_to_fogSD>

Note: Each variable is explained in the inference.py file.

Use Own Classifiers

To use your custom classifier, please follow the following steps:

1. Write the code for your classifier architecture in the classifier.py file in the models folder.
2. Now define the object of your classifier in the classification_inference method inside the conditionalDehazing.py file under the models folder.
3. Finally, define the weights of your classifier inside the inference.py file

To use your custom specialized dehazers, please follow the following steps:

1. Write the code for your classifier architecture in the dehazer.py file in the models folder.
2. Now define the object of your dehazer in the load_model method inside the helper.py file under the utils folder.
3. Finally, define the weights of your classifier inside the inference.py file

HazeSpace2M Folder Structure