UniformGaussianNoise#

class deepinv.physics.UniformGaussianNoise(sigma_min=0.0, sigma_max=0.5, rng: Generator | None = None)[source]#

Bases: NoiseModel

Gaussian noise \(y=z+\epsilon\) where \(\epsilon\sim \mathcal{N}(0,I\sigma^2)\) and \(\sigma \sim\mathcal{U}(\sigma_{\text{min}}, \sigma_{\text{max}})\)

Examples:

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

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

  • sigma_min (float) – minimum standard deviation of the noise.

  • sigma_max (float) – maximum standard deviation of the noise.

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

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

Adds the noise to measurements x.

Parameters:

  • x (torch.Tensor) – measurements.

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

Returns:

noisy measurements.