PoissonNoise

class deepinv.physics.PoissonNoise(gain=1.0, normalize=True, clip_positive=False, rng: Generator | None = None)[source]

Bases: NoiseModel

Poisson noise \(y = \mathcal{P}(\frac{x}{\gamma})\) with gain \(\gamma>0\).

If normalize=True, the output is divided by the gain, i.e., \(\tilde{y} = \gamma y\).

Examples:

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

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

Parameters:

gain (float) – gain of the noise.
normalize (bool) – normalize the output.
clip_positive (bool) – clip the input to be positive before adding noise. This may be needed when a NN outputs negative values e.g. when using LeakyReLU.
rng (torch.Generator (Optional)) – a pseudorandom random number generator for the parameter generation.

forward(x, gain=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 noise level.
seed (int) – the seed for the random number generator, if rng is provided.

Returns: