Blur#

class deepinv.physics.Blur(filter=None, padding='valid', use_fft=False, device=torch.device('cpu'), **kwargs)[source]#

Bases: LinearPhysics

Blur operator.

This forward operator performs

\[y = w*x\]

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

Parameters:

filter (torch.Tensor) – Tensor of size (b, 1, h, w) or (b, c, h, w) in 2D; (b, 1, d, h, w) or (b, c, d, h, w) in 3D, containing the blur filter, e.g., deepinv.physics.blur.gaussian_blur().
padding (str) – options are 'valid', 'circular', 'replicate' and 'reflect'. If padding='valid' the blurred output is smaller than the image (no padding) otherwise the blurred output has the same size as the image. (default is 'valid'). Only padding='valid' and padding = 'circular' are implemented in 3D.
use_fft (bool) – whether to use FFT-based convolutions. If True, it uses FFT-based convolutions which can be faster for large kernels. If False, it uses the standard convolution functions from torch.nn.functional.
device (torch.device, str) – Device on which the physics’ buffers will be created. When a buffer is updated via physics.update_parameters(), if the current buffer is None, use the device of the incoming value, else, the incoming value is casted to the device of the current buffer. To change the device of all buffers, please use physics.to(device).

Note

This class makes it possible to change the filter at runtime by passing a new filter to the forward method, e.g., y = physics(x, w). The new filter \(w\) is stored as the current filter.

Note

This class performs a true convolution, not a cross-correlation as the standard convolution functions in torch.nn.functional. It is recommended to use use_fft=True for large filters, and use_fft=False for small filters, since FFT-based convolutions can be faster for large kernels but slower for small kernels.

Examples:

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

>>> from deepinv.physics import Blur
>>> x = torch.zeros((1, 1, 16, 16)) # Define black image of size 16x16
>>> x[:, :, 8, 8] = 1 # Define one white pixel in the middle
>>> w = torch.ones((1, 1, 2, 2)) / 4 # Basic 2x2 averaging filter
>>> physics = Blur(filter=w)
>>> y = physics(x)
>>> 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 filter to the input image.

Parameters:

x (torch.Tensor) – input image.
filter (torch.Tensor) – Filter \(w\) to be applied to the input image. If not None, it uses this filter instead of the one defined in the class, and the provided filter is stored as the current filter.

Raises:

ValueError – if the input tensor does not have 4 or 5 dimensions.

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

Adjoint operator of the blur operator.

Parameters:

y (torch.Tensor) – blurred image.
filter (torch.Tensor) – Filter \(w\) to be applied to the input image. If not None, it uses this filter instead of the one defined in the class, and the provided filter is stored as the current filter.

Raises:

ValueError – if the input tensor does not have 4 or 5 dimensions.

Examples using `Blur`:#

Imaging inverse problems with adversarial networks

Calibrating physics operators

Distributed Physics Operators

Distributed Plug-and-Play (PnP) Reconstruction

Tour of blur operators

3D diffraction PSF

Tour of forward sensing operators

Spatial unwrapping and modulo imaging

Reducing the memory and computational complexity of unfolded network training

Blur#

Examples using Blur:#

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Examples using `Blur`:#