A Pytorch implementation of RetinexNet

Unofficial PyTorch code for the paper - Deep Retinex Decomposition for Low-Light Enhancement, BMVC'18 (Oral)

Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu

The offical Tensorflow code is available here.

Please ensure that you cite the paper if you use this code:

@inproceedings{Chen2018Retinex,
 title={Deep Retinex Decomposition for Low-Light Enhancement},
 author={Chen Wei, Wenjing Wang, Wenhan Yang, Jiaying Liu},
 booktitle={British Machine Vision Conference},
 year={2018},
 organization={British Machine Vision Association}
}

Please download the training and testing datasets from here.

Data folder should like:
-- data_name(Eg. LOL)
  -- train
    -- low
    -- high
 -- test
    -- low
    -- high

And just run

$ python train.py \

For sample testing/prediction, you can run-

$ python predict.py

There is a pre-trained checkpoint available in the repo. You may use it for sample testing or create your own after training as needed. The results are generated (by default) for the data present in ./data/test/low/ folder, and the results are saved (by default) in ./results/test/low/ folder.