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Tensorflow implementation of Perception-Preserving Convolutional Networks for Image Enhancement on Smartphones (ECCV 2018 Workshop PIRM)

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Python 3.5

PPCN (Team: Rainbow)

[Paper_download][Paper_CVF][Paper_Springer]

Super-Resolution Task


The schematics of the proposed network for image super-resolution

Training

First, download the DIV2K dataset and unzip it in train_SR/ folder.

Run the following command to train the SR model

python train_SR.py

Testing

First, download the SR_Test_Datasets and put them in test/SR_test_data folder.

Run the following command to super-resolve low-resolution images

python evaluate_super_resolution.py

Enhancement Task


DPED image enhanced by our method


The structure of the proposed generator and discriminator for image enhancement

Training

  • Step1: download the pre-trained VGG19 model and put it into train/vgg_pretrained/ folder
  • Step2: download DPED dataset and extract it into train/dped/ folder.
  • Step3: train the teacher model by executing the following command
python train_teacher.py
  • Step4: train the student model by running
python train_student.py

Testing

Run the following command to enhance low-quality images

python evaluate_enhancement.py

Citation

If you find PPCN useful in your research, please consider citing:

@inproceedings{Hui-PPCN-2018,
  title={Perception-Preserving Convolutional Networks for Image Enhancement on Smartphones},
  author={Hui, Zheng and Wang, Xiumei and Deng, Lirui and Gao, Xinbo},
  booktitle={ECCV Workshop},
  pages = {197--213},
  year={2018}
}

Code References

[1]https://github.com/aiff22/ai-challenge

[2]https://github.com/aiff22/DPED

[3]https://github.com/roimehrez/contextualLoss

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Tensorflow implementation of Perception-Preserving Convolutional Networks for Image Enhancement on Smartphones (ECCV 2018 Workshop PIRM)

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