Pansharpen
- class deepinv.physics.Pansharpen(img_size, filter='bilinear', factor=4, noise_color=GaussianNoise(), noise_gray=GaussianNoise(), device='cpu', padding='circular', **kwargs)[source]
Bases:
LinearPhysics
Pansharpening forward operator.
The measurements consist of a high resolution grayscale image and a low resolution RGB image, and are represented using
deepinv.utils.TensorList
, where the first element is the RGB image and the second element is the grayscale image.By default, the downsampling is done with a gaussian filter with standard deviation equal to the downsampling, however, the user can provide a custom downsampling filter.
It is possible to assign a different noise model to the RGB and grayscale images.
- Parameters:
filter (torch.Tensor, str, NoneType) – Downsampling filter. It can be ‘gaussian’, ‘bilinear’ or ‘bicubic’ or a custom
torch.Tensor
filter. IfNone
, no filtering is applied.factor (int) – downsampling factor.
noise_color (torch.nn.Module) – noise model for the RGB image.
noise_gray (torch.nn.Module) – noise model for the grayscale image.
padding (str) – options are
'valid'
,'circular'
,'replicate'
and'reflect'
. Ifpadding='valid'
the blurred output is smaller than the image (no padding) otherwise the blurred output has the same size as the image.
- Examples:
Pansharpen operator applied to a random 32x32 image:
>>> from deepinv.physics import Pansharpen >>> x = torch.randn(1, 3, 32, 32) # Define random 32x32 color image >>> physics = Pansharpen(img_size=x.shape[1:], device=x.device) >>> x.shape torch.Size([1, 3, 32, 32]) >>> y = physics(x) >>> y[0].shape torch.Size([1, 3, 8, 8]) >>> y[1].shape torch.Size([1, 1, 32, 32])
- A(x, **kwargs)[source]
Computes forward operator \(y = A(x)\) (without noise and/or sensor non-linearities)
- Parameters:
x (torch.Tensor,list[torch.Tensor]) – signal/image
- Returns:
(torch.Tensor) clean measurements
- A_adjoint(y, **kwargs)[source]
Computes transpose of the forward operator \(\tilde{x} = A^{\top}y\). If \(A\) is linear, it should be the exact transpose of the forward matrix.
Note
If the problem is non-linear, there is not a well-defined transpose operation, but defining one can be useful for some reconstruction networks, such as
deepinv.models.ArtifactRemoval
.- Parameters:
y (torch.Tensor) – measurements.
params (None, torch.Tensor) – optional additional parameters for the adjoint operator.
- Returns:
(torch.Tensor) linear reconstruction \(\tilde{x} = A^{\top}y\).
- forward(x, **kwargs)[source]
Computes forward operator
\[y = N(A(x), \sigma)\]- Parameters:
x (torch.Tensor, list[torch.Tensor]) – signal/image
- Returns:
(torch.Tensor) noisy measurements
Examples using Pansharpen
:
Stacking and concatenating forward operators.
A tour of forward sensing operators