This is a PyTorch implementation of the TIP2017 paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. The author's MATLAB implementation is here.

This code was written with PyTorch<0.4, but most people must be using PyTorch>=0.4 today. Migrating the code is easy. Please refer to PyTorch 0.4.0 Migration Guide.

• PyTorch(<0.4)
• torchvision
• OpenCV for Python
• HDF5 for Python
• tensorboardX (TensorBoard for PyTorch)

python train.py \
  --preprocess True \
  --num_of_layers 17 \
  --mode S \
  --noiseL 25 \
  --val_noiseL 25

NOTE

• If you've already built the training and validation dataset (i.e. train.h5 & val.h5 files), set preprocess to be False.
• According to the paper, DnCNN-S has 17 layers.
• noiseL is used for training and val_noiseL is used for validation. They should be set to the same value for unbiased validation. You can set whatever noise level you need.

python train.py \
  --preprocess True \
  --num_of_layers 20 \
  --mode B \
  --val_noiseL 25

NOTE

• If you've already built the training and validation dataset (i.e. train.h5 & val.h5 files), set preprocess to be False.
• According to the paper, DnCNN-B has 20 layers.
• noiseL is ingnored when training DnCNN-B. You can set val_noiseL to whatever you need.

python test.py \
  --num_of_layers 17 \
  --logdir logs/DnCNN-S-15 \
  --test_data Set12 \
  --test_noiseL 15

NOTE

• Set num_of_layers to be 17 when testing DnCNN-S models. Set num_of_layers to be 20 when testing DnCNN-B model.
• test_data can be Set12 or Set68.
• test_noiseL is used for testing. This should be set according to which model your want to test (i.e. logdir).

• Parameter initialization:
  Use kaiming_normal initialization for Conv; Pay attention to the initialization of BatchNorm

def weights_init_kaiming(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
    elif classname.find('Linear') != -1:
        nn.init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
    elif classname.find('BatchNorm') != -1:
        m.weight.data.normal_(mean=0, std=math.sqrt(2./9./64.)).clamp_(-0.025,0.025)
        nn.init.constant(m.bias.data, 0.0)

• The definition of loss function
  Set size_average to be False when defining the loss function. When size_average=True, the pixel-wise average will be computed, but what we need is sample-wise average.

criterion = nn.MSELoss(size_average=False)

The computation of loss will be like:

loss = criterion(out_train, noise) / (imgn_train.size()[0]*2)

where we divide the sum over one batch of samples by 2N, with N being # samples.