The project is the official implementation of our BMVC 2020 paper, "Towards Fast and Light-Weight Restoration of Dark Images"

We show that we can enhance High Resolution,2848×4256, extremely dark single-image in the ballpark of 3 seconds even on a CPU. We achieve this with 2−7× fewer model parameters, 2−3× lower memory utilization, 5−20× speed up and yet maintain a competitive image reconstruction quality compared to the state-of-the-art algorithms.

Watch the below video for results and overview of LLPackNet.

The train.py and test.py files were used for training and testing. Relevant comments have been added to these files for better understanding. You however need to download the SID dataset in your PC to execute them.

The Jupyter Notebooks containing test code for the ablation studies can be also found in the ablations folder.

We used PyTorch version 1.3.1 with Python 3.7 to conduct the experiment. Along with the commonly used Python libraries such Numpy and Skimage, do install the Rawpy library required to read RAW images.

If you find any information provided here useful please cite us,

@inproceedings{lamba2020LLPackNet,
  title={Towards Fast and Light-Weight Restoration of Dark Images},
  author={Lamba, Mohit and Balaji, Atul and Mitra, Kaushik},
  booktitle={British Machine Vision Conference (BMVC) 2020},
  year={2020},
  organization={BMVC}
}