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Flare7K: A Phenomenological Nighttime Flare Removal Dataset (NeurIPS 2022)

Paper | Project Page | Video

Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Chen Change Loy

S-Lab, Nanyang Technological University

Flare7K

Flare7K, the first nighttime flare removal dataset, which is generated based on the observation and statistic of real-world nighttime lens flares. It offers 5,000 scattering flare images and 2,000 reflective flare images, consisting of 25 types of scattering flares and 10 types of reflective flares. The 7,000 flare patterns can be randomly added to the flare-free images, forming the flare-corrupted and flare-free image pairs.

Update

  • 2022.02.09: Our training code is released.
  • 2022.12.28: The MIPI Workshop 2023 is released now. Our dataset serves as a track in this challenge. Please check the CodaLab page to find more details about our challenge.
  • 2022.10.12: Upload a flare-corrupted test dataset without ground truth.
  • 2022.10.11: Upload the dataset and pretrained model in Baidu Netdisk.
  • 2022.10.09: Update baseline inference code for flare removal.
  • 2022.09.16: Our paper Flare7K: A Phenomenological Nighttime Flare Removal Dataset is accepted by the NeurIPS 2022 Track Datasets and Benchmarks. 🤗
  • 2022.08.27: Update dataloader for our dataset.
  • 2022.08.25: Increase the number of test images from 20 to 100. Please download the latest version of our Flare7K dataset.
  • 2022.08.19: This repo is created.

Data Download

Baidu Netdisk Google Drive Number Description
Flares link link 7,000 We offers 5,000 scattering flare images and 2,000 reflective flare images, consisting of 25 types of scattering flares and 10 types of reflective flares.
Background Images link link 23,949 The background images are sampled from [Single Image Reflection Removal with Perceptual Losses, Zhang et al., CVPR 2018]. We filter our most of the flare-corrupted images and overexposed images.
Flare-corrupted images link link 645 We offer an extra flare-corrupted dataset without ground truth. It contains 645 images captured by different cameras and some images are very challenging.

Paired Data Generation

We provide a on-the-fly dataloader function and a flare-corrupted/flare-free pairs generation script in this repository. To use this function, please put the Flare7K dataset and 24K Flickr dataset on the same path with the generate_flare.ipynb file.

If you only want to generate the flare-corrupted image without reflective flare, you can comment out the following line:

# flare_image_loader.load_reflective_flare('Flare7K','Flare7k/Reflective_Flare')

Pretrained Model

The inference code based on Uformer is released Now. Your can download the pretrained checkpoints on [GoogleDrive | Baidu Netdisk]. Please place it under the experiments folder and unzip it, then you can run the deflare.ipynb for inference. We provide two models, the model in the folder uformer can help remove both the reflective flares and scattering flares. The uformer_noreflection one can only help remove the scattering flares but is more robust.

Evaluation Code

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

python evaluate.py --input result/blend/ --gt dataset/Flare7k/test_data/real/gt/

Training model

Training with single GPU

To train a model with your own data/model, you can edit the options/uformer_flare7k_option.yml and run the following codes. You can also add --debug command to start the debug mode:

python basicsr/train.py -opt options/uformer_flare7k_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/uformer_flare7k_option.yml

Flare7k structure

├── Flare7k
    ├── Reflective_Flare 
    ├── Scattering_Flare
         ├── Compound_Flare
         ├── Glare_with_shimmer
         ├── Light_Source
         ├── Streak
         
    ├── test_data
         ├── real
              ├── input
              ├── gt
         ├── synthetic
              ├── input
              ├── gt

License

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.

Citation

If you find this work useful, please cite:

@inproceedings{dai2022flare7k,
  title={Flare7K: A Phenomenological Nighttime Flare Removal Dataset},
  author={Dai, Yuekun and Li, Chongyi and Zhou, Shangchen and Feng, Ruicheng and Loy, Chen Change},
  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year={2022}
}

Contact

If you have any question, please feel free to reach me out at [email protected].