SCUNet

class deepinv.models.SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64, drop_path_rate=0.0, input_resolution=256, pretrained='download', device='cpu')

Bases: Denoiser

SCUNet denoising network.

The Swin-Conv-UNet (SCUNet) denoising was introduced in Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis.

Parameters:

• in_nc (int) – number of input channels. Default: 3.

• config (list) – number of layers in each stage. Default: [4, 4, 4, 4, 4, 4, 4].

• dim (int) – number of channels in each layer. Default: 64.

• drop_path_rate (float) – drop path per sample rate (stochastic depth) for each layer. Default: 0.0.

• input_resolution (int) – input resolution. Default: 256.

• pretrained (bool) – use a pretrained network. If pretrained=None, the weights will be initialized at random using Pytorch’s default initialization. If pretrained='download', the weights will be downloaded from an online repository (only available for the default architecture). Finally, pretrained can also be set as a path to the user’s own pretrained weights. Default: ‘download’. See pretrained-weights for more details.

• train (bool) – training or testing mode. Default: False.

• device (str) – gpu or cpu. Default: ‘cpu’.

forward(x, sigma=None, **kwargs)

Applies denoiser \(\denoiser{x}{\sigma}\).

Parameters:

• x (torch.Tensor) – noisy input.

• sigma (torch.Tensor, float) – noise level.

Returns:

(torch.Tensor) Denoised tensor.