C3Net
This is a PyTorch implementation of the New Trends in Image Restoration and Enhancement workshop and challenges on image and video restoration and enhancement (NTIRE 2020 with CVPR 2020) paper, C3Net: Demoireing Network Attentive in Channel, Color and Concatenation.
If you find our project useful in your research, please consider citing:
@InProceedings{Kim_2020_CVPR_Workshops,
author = {Kim, Sangmin and Nam, Hyungjoon and Kim, Jisu and Jeong, Jechang},
title = {C3Net: Demoireing Network Attentive in Channel, Color and Concatenation},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}
Dependencies
Python 3.6.9
PyTorch 1.4.0
Data
Proposed algorithm
Training
Use the following command to use our training codes
python train.py
For training pre-trained model, download the model first.
trained model (Track 1: Single Image)
trained model (Track 2: Burst)
(Trained model was deleted because there is no space to save them.)
Then, set the option --resume to where the downloaded model is.
There are other options you can choose.
Please refer to train.py.
Test
Use the following command to use our test codes
python test.py
For testing pre-trained model, download the model first.
trained model (Track 1: Single Image)
trained model (Track 2: Burst)
(Trained model was deleted because there is no space to save them.)
Then, set the option --logdir to where the downloded model is.
There are other options you can choose.
Please refer to test.py.
Results (PSNR/SSIM)
Track 1: Single Image - 41.30/0.99
Track 2: Burst - 40.55/0.99
Contact
If you have any question about the code or paper, feel free to ask me to [email protected].
Acknowledgement
Thanks for SaoYan who gave the implementaion of DnCNN.