python demo_enhance_CPGADN.py --net_name CPGA_DIA-lolv1 --use_gpu true --gpu 0 --val_ori_data_path LOLdataset/eval/eval15/low/ --val_haze_data_path LOLdataset/eval/eval15/low/ --dataset_type LOL-v1 --num_workers 1 --val_batch_size 1 --ckpt CPGA_DIA.pkl
// val_haze_data_path & val_ori_data_path keep the same input and use dataset_type LOL-v1

python demo_enhanced_video_CPGADN.py --use_gpu true --gpu 0 --output_name test --video_dir YOUR_VIDEO.mov --num_workers 0 --val_batch_size 1 --ckpt CPGA_DIA.pkl

Weng, SE., Hsu, CP., Hsiao, CY. et al. From dim to glow: dynamic illuminance adjustment for simultaneous exposure correction and low-light image enhancement. SIViP (2024). https://doi.org/10.1007/s11760-024-03519-0

@article{weng2024dim,
  title={From dim to glow: dynamic illuminance adjustment for simultaneous exposure correction and low-light image enhancement},
  author={Weng, Shyang-En and Hsu, Chang-Pin and Hsiao, Cheng-Yen and Christanto, Ricky and Miaou, Shaou-Gang},
  journal={Signal, Image and Video Processing},
  pages={1--11},
  year={2024},
  publisher={Springer}
}

Dynamic illuminance adjustment  
Low-light image enhancement  
Exposure correction  
Multi-task learning  
Advanced driver assistance systems