spyrit.core.prep.SplitPoisson

class spyrit.core.prep.SplitPoisson(alpha: float, meas_op)[source]

Bases: DirectPoisson

Preprocess the raw data acquired with a split measurement operator assuming Poisson noise. It also compensates for the affine transformation applied to the images to get positive intensities.

It computes

\[m = \frac{y_{+}-y_{-}}{\alpha} - H1\]

and the variance

\[\sigma^2 = \frac{2(y_{+} + y_{-})}{\alpha^{2}},\]

where \(y_{+} = H_{+}x\) and \(y_{-} = H_{-}x\) are obtained using a split measurement operator (see spyrit.core.LinearSplit), \(\alpha\) is the image intensity, and 1 is the all-ones vector.

Args:

alpha (float): maximun image intensity \(\alpha\) (in counts)

meas_op: measurement operator (see meas)

Example:

>>> H = torch.rand([400,32*32])
>>> meas_op =  LinearSplit(H)
>>> split_op = SplitPoisson(10, meas_op)

Example 2:

>>> Perm = torch.rand([32,32])
>>> meas_op = HadamSplit(400, 32,  Perm)
>>> split_op = SplitPoisson(10, meas_op)

Methods

`denormalize_expe`(x, beta[, h, w])	Denormalize images from the range [-1;1] to the range [0; \(\beta\)]
`forward`(x)	Preprocess to compensates for image normalization and splitting of the measurement operator.
`forward_expe`(x, meas_op)	Preprocess to compensate for image normalization and splitting of the measurement operator.
`set_expe`([gain, mudark, sigdark, nbin])	Sets experimental parameters of the sensor
`sigma`(x)	Estimates the variance of raw measurements
`sigma_expe`(x)	Estimates the variance of the measurements that are compensated for splitting but NOT for image normalization
`sigma_from_image`(x, meas_op)	Estimates the variance of the preprocessed measurements corresponding to images through a measurement operator
`unsplit`(x[, mode])	Unsplits measurements by combining odd and even indices.