spyrit.core.prep.DirectPoisson.sigma

DirectPoisson.sigma(x: tensor) → tensor

Estimates the variance of the preprocessed measurements

The variance is estimated as \(\frac{4}{\alpha^2} x\)

Args:

x: batch of measurement vectors

Shape:

x: \((B,M)\) where \(B\) is the batch dimension

Output: \((B, M)\)

Example:

>>> x = torch.rand([10,400], dtype=torch.float)
>>> v = prep_op.sigma(x)
>>> print(v.shape)
torch.Size([10, 400])