Code for "CRetinex: A Progressive Color-shift Aware Retinex Model for Low-light Image Enhancement".

This method can keep the color constancy of the low-light image (as can be seen from the enhancement results of the low-light images captured of the same scene).

The framework of this method is shown below:

python=3.6
tensorflow-gpu=1.14.0
numpy=1.19
scikit-image=0.17.2
pillow=8.2

• Training dataset:

  • Download the training data: LOL, AGLIE, and SID datasets.
  • Select part of the data for training, and put the low-light images and corresponding normal-light images in ./dataset/low/ and ./dataset/high/, respectively.
  • Can also put a small number of paired low-light and normal-light images in ./dataset/eval/low/ and ./dataset/eval/high/ for validation during the training phase.

• Train the decomposition network:
  

  • Run CUDA_VISIBLE_DEVICES=0 python train_decomposition_network.py
    
  • The relevant files are stored in ./checkpoint/decom/, ./logs/decom/, and ./eval_result/decom/

• Train the color shift estimation network:
  

  • Run CUDA_VISIBLE_DEVICES=0 python train_color_network.py
    
  • The relevant files are stored in ./checkpoint/color_net/, ./logs/color_net/, and ./eval_result/color/

• Train the spatially variant pollution estimation network:
  

  • Run CUDA_VISIBLE_DEVICES=0 python train_noise_network.py
    
  • The relevant files are stored in ./checkpoint/noise_net/, ./logs/noise_net/, and ./eval_result/noise/

• Train the illumination adjustment network:
  

  • Run CUDA_VISIBLE_DEVICES=0 python train_illu_adjust_network.py
    
  • The relevant files are stored in ./checkpoint/illu_adjust/, ./logs/illu_adjust/, and ./eval_result/illu_adjust/

• Put the test data in ./test_images/
• Run CUDA_VISIBLE_DEVICES=0 python test.py
  

If this work is helpful to you, please cite it as:

@article{xu2024CRetinex,
  title={CRetinex: A Progressive Color-shift Aware Retinex Model for Low-light Image Enhancement},
  author={Xu, Han and Zhang, Hao and Yi, Xunpeng and Ma, Jiayi},
  journal={International Journal of Computer Vision},
  year={2024},
  publisher={Springer}
}