DCDicL for Image Denoising

Hongyi Zheng*, Hongwei Yong*, Lei Zhang, "Deep Convolutional Dictionary Learning for Image Denoising," in CVPR 2021. (* Equal contribution)

[paper] [supp]

The implementation of DCDicL is based on the awesome Image Restoration Toolbox [KAIR].

Requirement

• PyTorch 1.6+
• prettytable
• tqdm

Testing

Step 1

• Download pretrained models from [OneDrive].
• Unzip downloaded file and put the folders into ./release/denoising

Step 2

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.
• data/n_channels: 1 for greyscale and 3 for color.
• test/visualize: true for saving the noisy input/predicted dictionaries.

Step 3

python test_dcdicl.py

Training

Training code will be released soon.