Noise Model

The lowerbound value for log-variance.

If pixelwise, log-variance is computed for each pixel, else log-variance is not computed.

Maximum signal intensity expected in the image.

Minimum value of standard deviation allowed in the GMM. All values of standard deviation below this are clamped to this value.

Minimum signal intensity expected in the image.

Number of coefficients to describe the functional relationship between gaussian parameters and the signal. 2 implies a linear relationship, 3 implies a quadratic relationship and so on.

Number of gaussians used for the GMM.

Path to the file containing observation or respective numpy array.

Path to the directory where the trained noise model (*.npz) is saved in the train method.

Path to the file containing signal or respective numpy array.

Tolerance used in the computation of the noise model likelihood.

A [3*n_gaussian, n_coeff] sized array containing the values of the weights describing the GMM noise model, with each row corresponding to one parameter of each gaussian, namely [mean, standard deviation and weight]. Specifically, rows are organized as follows: - first n_gaussian rows correspond to the means - next n_gaussian rows correspond to the weights - last n_gaussian rows correspond to the standard deviations If weight=None, the weight array is initialized using the min_signal and max_signal parameters.

List of noise models, one for each target channel.

The mean of the data, used to unnormalize data for noise model evaluation. Shape is (target_ch,) (or (1, target_ch, [1], 1, 1)).

The standard deviation of the data, used to unnormalize data for noise model evaluation. Shape is (target_ch,) (or (1, target_ch, [1], 1, 1)).