GaussianNoise#

class deepinv.physics.GaussianNoise(sigma=0.1, rng: Generator | None = None)[source]#

Bases: NoiseModel

Gaussian noise \(y=z+\epsilon\) where \(\epsilon\sim \mathcal{N}(0,I\sigma^2)\).

Examples:

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

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

Parameters:

sigma (float) – Standard deviation of the noise.
rng (torch.Generator (Optional)) – a pseudorandom random number generator for the parameter generation.

forward(x, sigma=None, seed=None, **kwargs)[source]#

Adds the noise to measurements x

Parameters:

x (torch.Tensor) – measurements
sigma (float, torch.Tensor) – standard deviation 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: