spyrit.core.noise

Noise models for simulating measurements in imaging.

There are four classes in this module, that each simulate a different type of noise in the measurements. The classes simulate the following types of noise:

  • NoNoise: Simulates measurements with no noise

  • Poisson: Simulates measurements corrupted by Poisson noise (each pixel

    receives a number of photons that follows a Poisson distribution)

  • PoissonApproxGauss: Simulates measurements corrupted by Poisson noise, but

    approximates the Poisson distribution with a Gaussian distribution

  • PoissonApproxGaussSameNoise: Simulates measurements corrupted by Poisson

    noise, but all measurements in a batch are corrupted with the same noise sample (approximated by a Gaussian distribution)

Classes

NoNoise(meas_op)

Simulates measurements from images in the range [0;1] by computing \(y = \frac{1}{2} H(1+x)\).

Poisson(meas_op[, alpha])

Simulates measurements corrupted by Poisson noise

PoissonApproxGauss(meas_op, alpha)

Simulates measurements corrupted by Poisson noise.

PoissonApproxGaussSameNoise(meas_op, alpha)

Simulates measurements corrupted by Poisson noise.