Code of "DBDnet: A Deep Boosting Strategy for Image Denoising"
This code was tested with Python 2.7. It is highly recommended to use the GPU version of Tensorflow for fast training.
natsort==5.4.1
numpy==1.14.5
tensorflow==1.10.0
Pillow==5.4.1
First, 128x3000 patches are extracted from the CBSD432 images as follows:
python2 generate_patches_rgb_blind.py
Then train the network:
python2 main_blind.py --phase train
You can also control other paramaters such as batch size, number of epochs. More info inside main.py.
The checkpoints are saved in ./checkpoint folder. Denoised validation images are saved after each epoch in ./sample folder.
tensorboard --logdir=./logs
To test the network for sigma=50:
python2 main_blind.py --phase test --sigma 50.0
Denoised images are saved in ./test folder.
- Code structure follows the repository DnCNN-Tensorflow