BlurFFT#

class deepinv.physics.BlurFFT(img_size, filter=None, device='cpu', **kwargs)[source]#

Bases: DecomposablePhysics

FFT-based blur operator.

It performs the operation

\[y = w*x\]

where \(*\) denotes convolution and \(w\) is a filter.

Blur operator based on torch.fft operations, which assumes a circular padding of the input, and allows for the singular value decomposition via deepinv.Physics.DecomposablePhysics and has fast pseudo-inverse and prox operators.

Parameters:

img_size (tuple) – Input image size in the form (C, H, W).
filter (torch.Tensor) – torch.Tensor of size (1, c, h, w) containing the blur filter with h<=H, w<=W and c=1 or c=C e.g., deepinv.physics.blur.gaussian_filter().
device (str) – cpu or cuda

Examples:

BlurFFT operator with a basic averaging filter applied to a 16x16 black image with a single white pixel in the center:

>>> from deepinv.physics import BlurFFT
>>> x = torch.zeros((1, 1, 16, 16)) # Define black image of size 16x16
>>> x[:, :, 8, 8] = 1 # Define one white pixel in the middle
>>> filter = torch.ones((1, 1, 2, 2)) / 4 # Basic 2x2 filter
>>> physics = BlurFFT(filter=filter, img_size=(1, 1, 16, 16))
>>> y = physics(x)
>>> y[y<1e-5] = 0.
>>> y[:, :, 7:10, 7:10] # Display the center of the blurred image
tensor([[[[0.2500, 0.2500, 0.0000],
          [0.2500, 0.2500, 0.0000],
          [0.0000, 0.0000, 0.0000]]]])