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Deep Recursive HDRI in Pytorch

paper

We provide PyTorch implementations for GAN-based mutliple exposure stack generation.

  • Deep recursive HDRI

General

If you use the code for your research work, please cite our papers.

@inproceedings{lee2018deep,
  title={Deep recursive hdri: Inverse tone mapping using generative adversarial networks},
  author={Lee, Siyeong and Hwan An, Gwon and Kang, Suk-Ju},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={596--611},
  year={2018}
}

Model inference

  • Conda environment
conda create -n hdr python=3.6
conda activate hdr
conda install -c anaconda mkl
conda install pytorch==1.0.0 torchvision==0.2.1 cuda100 -c pytorch
  • install requirements.txt
pip install -r requirements.txt
  • Please download two model weights below and organize the downloaded files as follows:
DeepRecursive_HDRI
├──Result
    └──model
       ├── HDRGAN_stopdown_G_param_ch3_batch1_epoch20_lr0.0002.pkl
       └── HDRGAN_stopup_G_param_ch3_batch1_epoch20_lr0.0002.pkl
  • Prepare your test images
DeepRecursive_HDRI
├──input
   ├── t10.png 
   ├── t11.png
  • Run the pretrained model
python test.py --test_dataset './input'
  • output
DeepRecursive_HDRI
├──Result
   ├── t10 (multi exposure stack)
   ├── t11 (multi exposure stack)

Note: We used the HDR Toolbox implementation of [Debevec and Malik 1997] to generate the results in our paper.

Model weight

Model Name model weight
Deep Recursive HDRI stopdown
stopup

License

Copyright (c) 2020, Siyeong Lee. All rights reserved.

The code is distributed under a BSD license. See LICENSE for information.

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