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_blur().
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, 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]]]])

A(x, filter=None, **kwargs)[source]#

Applies the forward operator \(y = A(x)\).

If a mask/singular values is provided, it is used to apply the forward operator, and also stored as the current mask/singular values.

Parameters:

x (torch.Tensor) – input tensor
mask (torch.nn.parameter.Parameter, float) – singular values.

Returns:

output tensor

A_adjoint(x, filter=None, **kwargs)[source]#

Computes the adjoint of the forward operator \(\tilde{x} = A^{\top}y\).

If a mask/singular values is provided, it is used to apply the adjoint operator, and also stored as the current mask/singular values.

Parameters:

y (torch.Tensor) – input tensor
mask (torch.nn.parameter.Parameter, float) – singular values.

Returns:

output tensor

U(x)[source]#

Applies the \(U\) operator of the SVD decomposition.

Note

This method should be overwritten by the user to define its custom DecomposablePhysics operator.

Parameters:: x (torch.Tensor) – input tensor

U_adjoint(x)[source]#

Applies the \(U^{\top}\) operator of the SVD decomposition.

Note

This method should be overwritten by the user to define its custom DecomposablePhysics operator.

Parameters:: x (torch.Tensor) – input tensor

V(x)[source]#

Applies the \(V\) operator of the SVD decomposition.

Note

This method should be overwritten by the user to define its custom DecomposablePhysics operator.

Parameters:: x (torch.Tensor) – input tensor

V_adjoint(x)[source]#

Applies the \(V^{\top}\) operator of the SVD decomposition.

Note

This method should be overwritten by the user to define its custom DecomposablePhysics operator.

Parameters:: x (torch.Tensor) – input tensor

update_parameters(filter=None, **kwargs)[source]#

Updates the current filter.

Parameters:: filter (torch.Tensor) – New filter to be applied to the input image.

Examples using `BlurFFT`:#

A tour of blur operators

Image deblurring with custom deep explicit prior.

Image deblurring with Total-Variation (TV) prior

DPIR method for PnP image deblurring.

Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.

Building your custom sampling algorithm.

Deep Equilibrium (DEQ) algorithms for image deblurring

BlurFFT#

Examples using BlurFFT:#

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Examples using `BlurFFT`:#