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:

noisy measurements

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

Updates the standard deviation of the noise.

Parameters:

sigma (float, torch.Tensor) – standard deviation of the noise.

Examples using GaussianNoise:

Reconstructing an image using the deep image prior.

Reconstructing an image using the deep image prior.

Image transforms for equivariance & augmentations

Image transforms for equivariance & augmentations

Creating your own dataset

Creating your own dataset

Creating a forward operator.

Creating a forward operator.

A tour of forward sensing operators

A tour of forward sensing operators

Image deblurring with custom deep explicit prior.

Image deblurring with custom deep explicit prior.

Random phase retrieval and reconstruction methods.

Random phase retrieval and reconstruction methods.

Image deblurring with Total-Variation (TV) prior

Image deblurring with Total-Variation (TV) prior

Image inpainting with wavelet prior

Image inpainting with wavelet prior

3D wavelet denoising

3D wavelet denoising

Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting

Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting

Vanilla PnP for computed tomography (CT).

Vanilla PnP for computed tomography (CT).

DPIR method for PnP image deblurring.

DPIR method for PnP image deblurring.

Regularization by Denoising (RED) for Super-Resolution.

Regularization by Denoising (RED) for Super-Resolution.

PnP with custom optimization algorithm (Condat-Vu Primal-Dual)

PnP with custom optimization algorithm (Condat-Vu Primal-Dual)

Uncertainty quantification with PnP-ULA.

Uncertainty quantification with PnP-ULA.

Image reconstruction with a diffusion model

Image reconstruction with a diffusion model

Building your custom sampling algorithm.

Building your custom sampling algorithm.

Implementing DiffPIR

Implementing DiffPIR

Self-supervised learning with measurement splitting

Self-supervised learning with measurement splitting

Self-supervised denoising with the UNSURE loss.

Self-supervised denoising with the UNSURE loss.

Vanilla Unfolded algorithm for super-resolution

Vanilla Unfolded algorithm for super-resolution

Deep Equilibrium (DEQ) algorithms for image deblurring

Deep Equilibrium (DEQ) algorithms for image deblurring

Learned Primal-Dual algorithm for CT scan.

Learned Primal-Dual algorithm for CT scan.