Multi-Modal Deep Convolutional Dictionary Learning for Image Denoising

Zhonggui Sun, Mingzhu Zhang, Huichao Sun, Jie Li, Tingting Liu, Xinbo Gao*, "Multi-Modal Deep Convolutional Dictionary Learning for Image Denoising," in Neurocomputing. (* Corresponding author)

The implementation of MMDCDicL is based on the DCDicL.

Requirement

• PyTorch 1.6+
• prettytable
• tqdm

Usage

Testing

Modify Parameter

Configure options/test_denoising.json. Important settings:

• task: task name.
• path/root: path to save the tasks.
• path/pretrained_netG: path to the folder containing the pretrained models.

Runing

python test_dcdicl.py

Training

Prepare training datasets

Prepare training/testing data. The folder structure should be similar to:

+-- data
|   +-- train
|       +-- Flash_non_Flash
|       +-- RGB_NIR
|   +-- test
|       +-- Flash_non_Flash
|       +-- RGB_NIR

Modify Parameter

Configure options/train_denoising.json. Important settings:

task: task name. path/root: path to save the tasks. data/train/sigma: range of noise levels. netG/d_size: dictionary size. netG/n_iter: number of iterations. netG/nc_x: number of channels in NetX. netG/nb: number of blocks in NetX. test/visualize: true for saving the noisy input/predicted dictionaries. If you want to reload a pretrained model, pay attention to following settings: path/pretrained_netG: path to the folder containing the pretrained models.

Runing

python train_dcdicl.py

Result

Quantitative Results

Qualitative Results

Acknowledgments

The authors would like to express their great thankfulness to the Associate Editor and the anonymous reviewers for their valuable comments and constructive suggestions. At the same time, they would like to express their heartfelt thanks to the authors of the open source DCDicL. We recommend reading this article DCDicL.

Citation