spyrit.core.recon.Denoise_layer

class spyrit.core.recon.Denoise_layer(M: int)

Bases: Module

Wiener filter that assumes additive white Gaussian noise.

\(y = \sigma_\text{prior}^2/(\sigma^2_\text{prior} + \sigma^2_\text{meas}) x\), where \(\sigma^2_\text{prior}\) is the variance prior and \(\sigma^2_\text{meas}\) is the variance of the measurement, \(x\) is the input vector and \(y\) is the output vector.

Args:

M (int): size of incoming vector

Shape:

• Input: \((*, M)\).

• Output: \((*, M)\).

Attributes:

weight: The learnable standard deviation prior \(\sigma_\text{prior}\) of shape \((M, 1)\). The values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where \(k = 1/M\).

in_features: The number of input features equal to \(M\).

Example:

>>> m = Denoise_layer(30)
>>> input = torch.randn(128, 30)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])

Methods

forward(inputs)	Applies a transformation to the incoming data: \(y = \sigma_\text{prior}^2/(\sigma_\text{prior}^2+x)\).
reset_parameters()	Resets the standard deviation prior \(\sigma_\text{prior}\).
tikho(inputs, weight)	Applies a transformation to the incoming data: \(y = \sigma_\text{prior}^2/(\sigma_\text{prior}^2+x)\).