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Yuekun Dai, Yihang Luo, Shangchen Zhou, Chongyi Li, Chen Change Loy

S-Lab, Nanyang Technological University

• 2023.06.13: Our training code is released.
• 2023.03.22: Our BracketFlare dataset and pretrained model is uploaded to the Google Drive and Baidu Netdisk now.
• 2023.03.21: Our paper is selected as a highlight (top2.5%) at CVPR 2023.
• 2023.02.28: Our paper Nighttime Smartphone Reflective Flare Removal using Optical Center Symmetry Prior is accepted by the CVPR2023 with 3 top ratings. 🤗

1. Clone the repo

   git clone https://github.com/ykdai/BracketFlare

2. Install dependent packages

   cd BracketFlare
   pip install -r requirements.txt

3. Install BracketFlare
   Please run the following commands in the BracketFlare root path to install BracketFlare:
   

   python setup.py develop

├── BracketFlare
    ├── flare
    ├── gt
    ├── test
         ├── flare
         ├── gt
         ├── mask
         ├── lq

The inference code based on MPRNet is released Now. You can download the pretrained checkpoints on [GoogleDrive | Baidu Netdisk]. Please place it under the experiments folder and unzip it.

To generate the flare-free images from the test/lq folder, you can run the test.py by using:

python test.py --i test/lq/ --o result/ --model_path experiments/net_g_last.pth

If you have a large image and don't want to change the size of it, you can also try test_large.py by using:

python test_large.py --i test/lq/ --o result/ --model_path experiments/net_g_last.pth --inpaint

Module --inpaint is mainly for tiny reflective flares, it will achieve smoother results for large size image.

To calculate different metrics with our pretrained model, you can run the evaluate.py by using:

python evaluate.py --input result/deflare/ --gt test/gt --mask test/mask

Training with single GPU

To train a model, you need to download and unzip our BracketFlare dataset under data folder. You may edit the options/bracketflare_mprnet_option.yml and run the following code. You can also add --debug command to start the debug mode:

python basicsr/train.py -opt options/bracketflare_mprnet_option.yml

Training with multiple GPU

You can run the following command for the multiple GPU tranining:

CUDA_VISIBLE_DEVICES=0,1 bash scripts/dist_train.sh 2 options/bracketflare_mprnet_option.yml

This project is licensed under S-Lab License 1.0. Redistribution and use of the dataset and code for non-commercial purposes should follow this license. This project is built on BasicSR, the detailed tutorial on training commands and config settings can be found here.

If you find this work useful, please cite:

@inproceedings{dai2023nighttime,
  title={Nighttime Smartphone Reflective Flare Removal using Optical Center Symmetry Prior},
  author={Dai, Yuekun and Luo, Yihang and Zhou, Shangchen and Li, Chongyi and Loy, Chen Change},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023}
 }

If you have any question (such as asking for the 4K version of our BracketFlare Dataset), please feel free to reach me out at [email protected].