SpaceVaryingBlur#

class deepinv.physics.SpaceVaryingBlur(filters=None, multipliers=None, padding='valid', device='cpu', **kwargs)[source]#

Bases: LinearPhysics

Space varying blur via product-convolution.

This linear operator performs

\[y = \sum_{k=1}^K h_k \star (w_k \odot x)\]

where \(\star\) is a convolution, \(\odot\) is a Hadamard product, \(w_k\) are multipliers \(h_k\) are filters.

Parameters:

  • filters (torch.Tensor) – Filters \(h_k\). Tensor of size (B, C, K, h, w) where B is the batch size, C the number of channels, K the number of filters, h and w the filter height and width which should be smaller or equal than the image \(x\) height and width respectively.

  • multipliers (torch.Tensor) – Multipliers \(w_k\). Tensor of size (B, C, K, H, W) where B is the batch size, C the number of channels, K the number of multipliers, H and W the image \(x\) height and width.

  • padding (str) – options = 'valid', 'circular', 'replicate', '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.

  • device (torch.device, str) – Device on which the physics’ buffers will be created. If a buffer is updated via physics.update_parameters(), if not None, it will be automatically casted to the device of the replaced buffer, else, use the device of the provided value. To change the device of all buffers, please use physics.to(device).

Examples:

We show how to instantiate a spatially varying blur operator.

>>> from deepinv.physics.generator import DiffractionBlurGenerator, ProductConvolutionBlurGenerator
>>> from deepinv.physics.blur import SpaceVaryingBlur
>>> from deepinv.utils.plotting import plot
>>> psf_size = 32
>>> img_size = (256, 256)
>>> delta = 16
>>> psf_generator = DiffractionBlurGenerator((psf_size, psf_size))
>>> pc_generator = ProductConvolutionBlurGenerator(psf_generator=psf_generator, img_size=img_size)
>>> params_pc = pc_generator.step(1)
>>> physics = SpaceVaryingBlur(**params_pc)
>>> dirac_comb = torch.zeros(img_size).unsqueeze(0).unsqueeze(0)
>>> dirac_comb[0,0,::delta,::delta] = 1
>>> psf_grid = physics(dirac_comb)
>>> plot(psf_grid, titles="Space varying impulse responses")
A(x, filters=None, multipliers=None, padding=None, **kwargs)[source]#

Applies the space varying blur operator to the input image.

It can receive new parameters \(w_k\), \(h_k\) and padding to be used in the forward operator, and stored as the current parameters.

Parameters:

  • filters (torch.Tensor) – Filters \(h_k\). Tensor of size (b, c, K, h, w). \(b \in \{1, B\}\) and \(c \in \{1, C\}\), \(h\leq H\) and \(w\leq W\).

  • multipliers (torch.Tensor) – Multipliers \(w_k\). Tensor of size (b, c, K, H, W). \(b \in \{1, B\}\) and \(c \in \{1, C\}\)

  • padding – options = 'valid', 'circular', 'replicate', '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.

  • device (str) – cpu or cuda

A_adjoint(y, filters=None, multipliers=None, padding=None, **kwargs)[source]#

Applies the adjoint operator.

It can receive new parameters \(w_k\), \(h_k\) and padding to be used in the forward operator, and stored as the current parameters.

Parameters:

  • filters (torch.Tensor) – Filters \(h_k\). Tensor of size (b, c, K, h, w). \(b \in \{1, B\}\) and \(c \in \{1, C\}\), \(h\leq H\) and \(w\leq W\).

  • multipliers (torch.Tensor) – Multipliers \(w_k\). Tensor of size (b, c, K, H, W). \(b \in \{1, B\}\) and \(c \in \{1, C\}\)

  • padding (str) – options = 'valid', 'circular', 'replicate', '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.

update_parameters(filters=None, multipliers=None, padding=None, **kwargs)[source]#

Updates the current parameters.

Parameters:

  • filters (torch.Tensor) – Filters \(h_k\). Tensor of size (b, c, K, h, w). \(b \in \{1, B\}\) and \(c \in \{1, C\}\), \(h\leq H\) and \(w\leq W\).

  • multipliers (torch.Tensor) – Multipliers \(w_k\). Tensor of size (b, c, K, H, W). \(b \in \{1, B\}\) and \(c \in \{1, C\}\)

  • padding (str) – options = 'valid', 'circular', 'replicate', 'reflect'.

Examples using SpaceVaryingBlur:#

Blind deblurring with kernel estimation network

Blind deblurring with kernel estimation network

Tour of blur operators

Tour of blur operators