Learnbale_Bandpass_Filter

Image Demoireing with Learnable Bandpass Filters, CVPR2020

Our extension work is accepted by IEEE TPAMI. The journal paper will come soon.

If you find this work is helpful, please cite:

@article{zheng2021learn,

title={Learning Frequency Domain Priors for Image Demoireing},

author = {Bolun, Zheng and Shanxin, Yuan and Chenggang, Yan and Xiang, Tian and Jiyong, Zhang and Yaoqi, Sun and Lin, Liu and Ales, Leonardis and Gregory, Slabaugh},

journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},

year={2021} }

@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={2020},

publisher={IEEE} }

You can now get this paper at Arxiv preprint: https://arxiv.org/abs/2004.00406

Run the code

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,

1. edit the 'main_multiscale.py' by: replacing the 'test_path', 'valid_gt_path', 'valid_ns_path' and 'weight_path' with your own settings.

2. make the dirs 'testing_result' and 'validation_result' at current path.

3. python main_multiscale.py.