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}
}
Python 3.6.9
PyTorch 1.4.0
You have to sign in Codalab and apply to NTIRE 2020 Demoireing Challenge before getting the data.
Use the following command to use our training codes
python train.py
There are other options you can choose. Please refer to train.py.
Use the following command to use our test codes
python test.py
There are other options you can choose. Please refer to test.py.
To use heavier model, we also used numpy to read input data, not hdf5.
Hyung-Joon and jisukim helped it.
Our best records can be derived in the code by changing h5 into numpy and reducing GPU memory.
| Validation Server | PSNR | SSIM | Rank |
|---|---|---|---|
| Track 1: Single Image | 41.30 | 0.99 | 9th |
| Track 2: Burst | 40.55 | 0.99 | 5th |
| Testing Server | PSNR | SSIM | Rank |
|---|---|---|---|
| Track 1: Single Image | 41.11 | 0.99 | 4th |
| Track 2: Burst | 40.33 | 0.99 | 5th |
If you have any question about Demoireing model and the CVPR2020 challenge paper, feel free to ask me to ksmh1652@gmail.com.
If you have any question about Deblurring model, visit here and feel free to ask Hyung-Joon to 013107nam@gmail.com.
If you have any question about using more heavier C3Net, visit here and feel free to ask jisukim to jisus.kim189@gmail.com.
Thanks for SaoYan who gave the implementaion of DnCNN.
Thanks for yun_yang who gave the implementation of DRRN.
Thanks for BumjunPark who gave the implementation of DHDN.
Hint of color loss from Jorge Pessoa.
Hint of concatenation and residual learning from RDN (informal implementation).
Hint of U-net block from DIDN (formal implementation).
C3Net started from RUN.
Also, we won 3rd Place in NTIRE 2020 Challenge on Image and Video Deblurring thanks to Hyung-Joon and jisukim.
The code is available at here.
