PoissonGaussianNoise

class deepinv.physics.PoissonGaussianNoise(gain=1.0, sigma=0.1, rng: Generator | None = None)[source]

Bases: NoiseModel

Poisson-Gaussian noise \(y = \gamma z + \epsilon\) where \(z\sim\mathcal{P}(\frac{x}{\gamma})\) and \(\epsilon\sim\mathcal{N}(0, I \sigma^2)\).

Examples:

Adding Poisson gaussian noise to a physics operator by setting the noise_model attribute of the physics operator:

>>> from deepinv.physics import Denoising, PoissonGaussianNoise
>>> import torch
>>> physics = Denoising()
>>> physics.noise_model = PoissonGaussianNoise()
>>> x = torch.rand(1, 1, 2, 2)
>>> y = physics(x)
Parameters:

  • gain (float) – gain of the noise.

  • sigma (float) – Standard deviation of the noise.

  • rng (torch.Generator (Optional)) – a pseudorandom random number generator for the parameter generation.

forward(x, gain=None, sigma=None, seed: int | None = None, **kwargs)[source]

Adds the noise to measurements x

Parameters:

  • x (torch.Tensor) – measurements

  • gain (None, float, torch.Tensor) – gain of the noise. If not None, it will overwrite the current gain.

  • sigma (None, float, torch.Tensor) – Tensor containing gain and standard deviation. If not None, it will overwrite the current gain and standard deviation.

  • seed (int) – the seed for the random number generator, if rng is provided.

Returns:

noisy measurements

update_parameters(gain=None, sigma=None, **kwargs)[source]

Updates the gain and standard deviation of the noise.

Parameters:

Examples using PoissonGaussianNoise:

A tour of forward sensing operators

A tour of forward sensing operators