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