Image Demoireing with Learnable Bandpass Filters, CVPR2020
If you find this work is helpful, please cite:
@inProceedings{zheng2020,
author={B. Zheng and S. Yuan and G. Slabaugh and A. Leonardis},
booktitle={IEEE Conference on Computer Vision and Pattern Recongnition},
title={Image Demoireing with Learnable Bandpass Filters},
year={2020},
}
@article{zheng2019implicit, title={Implicit dual-domain convolutional network for robust color image compression artifact reduction}, author={Zheng, Bolun and Chen, Yaowu and Tian, Xiang and Zhou, Fan and Liu, Xuesong}, journal={IEEE Transactions on Circuits and Systems for Video Technology}, volume={30}, number={11}, pages={3982--3994}, year={2019}, publisher={IEEE} }
You can now get this paper at Arxiv preprint: https://arxiv.org/abs/2004.00406
This project requires:
- Tensorflow >1.10
- Keras > 2.0
- opencv > 2.0
- skImage
You can get the weight file for AIM2019 via:
https://1drv.ms/u/s!ArU0YIIFiFuHilwyuwHZjSpvPUBz?e=iZ70Ga
or via Baidu Disk：
https://pan.baidu.com/s/1wsJYyYbQO-ETL5Jq4fN6hw code：jiae
You can get AIM2019 LCDMoire2019 dataset via:
validation:
Moire: https://data.vision.ee.ethz.ch/timofter/AIM19demoire/ValidationMoire.zip
Clean: https://data.vision.ee.ethz.ch/timofter/AIM19demoire/ValidationClear.zip
testing:
https://data.vision.ee.ethz.ch/timofter/AIM19demoire/TestingMoire.zip
Then,
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edit the 'main_multiscale.py' by: replacing the 'test_path', 'valid_gt_path', 'valid_ns_path' and 'weight_path' with your own settings.
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make the dirs 'testing_result' and 'validation_result' at current path.
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python main_multiscale.py.