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DENet: Disentangled Embedding Network for Visible Watermark Removal

This is official implementation for paper DENet: Disentangled Embedding Network for Visible Watermark Removal [AAAI2023 Oral]

Dataset preparation

|--data
|--|--LOGO
   |--|--10kmid
   |--|--10kgray
   |--|--10khigh

Pretrained Model

PSNR SSIM LPIPS
LOGO-L 44.24 0.9954 0.54
LOGO-H 40.83 0.9919 0.89
LOGO-Gray 42.60 0.9944 0.53

Installation

pip install -r requirements.txt

Training

Train on LOGO-H

bash scripts/train_contrast_attention_on_logo_high.sh

Train on LOGO-L

bash scripts/train_contrast_attention_on_logo_mid.sh

Train on LOGO-Gray

bash scripts/train_contrast_attention_on_logo_gray.sh

Testing

Test on LOGO-H

bash scripts/test_LOGO_10khigh.sh

Test on LOGO-L

bash scripts/test_LOGO_10kmid.sh

Test on LOGO-Gray

bash scripts/test_LOGO_10kgray.sh

Acknowledgement

This code is mainly based on the previous work SLBR.

denet's People

Contributors

flotingdream avatar lianchengmingjue avatar suyukun666 avatar

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