DIDN

Pytorch Implementation of "Deep Iterative Down-Up CNN for Image Denoising" [PDF]

Our method took second and third place at each track in [NTIRE 2019 Challenge on Real Image Denoising]

If you use our work in your research or publication, please cite our paper:

@inproceedings{yu2019deep,
  title={Deep iterative down-up CNN for image denoising},
  author={Yu, Songhyun and Park, Bumjun and Jeong, Jechang},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year={2019}

Related Work

Other method of our team "Densely Connected Hierarchical Network for Image Denoising" [PDF], [code]

Environment

python 3.6
pytorch 1.0.0
MATLAB (to prepare data)

Prepare data

You need to prepare data before training or testing the model.
Please use matlab files in './data' folder.
For training, we used DIV2K dataset, and only sample images are included in this project.
Please download the whole dataset for training the model. [DIV2K download page]

Test

Download Pre-trained models: [download]
and place them in the './checkpoint/pretrained_color' and './checkpoint/pretrained_gray' folders.
for grayscale images, gray_eval.py
for color images, color_eval.py

optional arguments:  
  --cuda : using cuda   
  --model : model path  
  --output_path : output image path   
  --self_ensemble : using self-ensemble  
  --gpus : gpu ids  
  
  You can also change test dataset in the code!

Example:

python gray_eval.py --cuda --self_ensemble

If you are interested in the ntire challenge results, you can download the pre-trained models [here]

Training

optional arguments:  
  --batchSize : batchsize (default: 16)  
  --nEpochs : number of epochs (default: 50)  
  --lr : initial learning rate (default: 0.0001)  
  --cuda : using cuda   
  --resume : path to checkpoint (default: none)  
  --start_epoch : manual starting epoch number (useful for resume, default: 1)  
  --threads : number of threads for data loader (default: 0)  
  --gpus : gpu ids  
  
  You can also change training data path and validation data path in the code!  

Example:

python gray_train.py --cuda --threads 8

Weight ensemble

For weight ensemble, train the model multiple times manually using --resume option,
and averaging weights using 'weight_ensemble_2.py'.

Contact

If you have any questions about the code or paper, please contact [email protected]