Real-Time Exposure Correction via Collaborative Transformations and Adaptive Sampling (CVPR 2024)

PyTorch Implementation of paper: Real-Time Exposure Correction via Collaborative Transformations and Adaptive Sampling.

Setup

• Install the conda environment

conda create -n cotf python=3.7
conda activate cotf

• Install Pytroch

# CUDA 10.2
conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 cudatoolkit=10.2 -c pytorch

• Install other requirements, such as basicsr.

pip install -r requirements.txt

Dataset

Please refer to the link below to download the dataset, place it in the . /datasets folder. Organize the data like the example in ./datasets/example. Then remember to modify the dataset options in OPTIONS accordingly.

• LCDP https://www.whyy.site/paper/lcdp
• MSEC https://github.com/mahmoudnafifi/Exposure_Correction
• SICE https://github.com/KevinJ-Huang/ExposureNorm-Compensation

Run

• Testing. The testing configuration is in options/test/. We put all the pre-training weights in the ./experiments. After running, the results of the visualization will be saved in . /results.

python test.py -opt options/test/test_lcdp.yml
python test.py -opt options/test/test_msec.yml
python test.py -opt options/test/test_sice.yml

• Training. The training configuration is in options/train/.

python train.py -opt options/train/train_lcdp.yml

Citation

Please cite the following paper if you feel our work useful to your research:

@InProceedings{li2024cotf,
    title     = {Real-Time Exposure Correction via Collaborative Transformations and Adaptive Sampling},
    author    = {Ziwen Li, Feng Zhang, Meng Cao, Jinpu Zhang, Yuanjie Shao, Yuehuan Wang, Nong Sang},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    year      = {2024}
}