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.
Image transforms for equivariance & augmentations
A tour of forward sensing operators
Image deblurring with custom deep explicit prior.
Random phase retrieval and reconstruction methods.
Image deblurring with Total-Variation (TV) prior
Image inpainting with wavelet prior
Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting
Vanilla PnP for computed tomography (CT).
DPIR method for PnP image deblurring.
Regularization by Denoising (RED) for Super-Resolution.
PnP with custom optimization algorithm (Condat-Vu Primal-Dual)
Uncertainty quantification with PnP-ULA.
Image reconstruction with a diffusion model
Building your custom sampling algorithm.
Self-supervised learning with measurement splitting
Self-supervised denoising with the UNSURE loss.
Vanilla Unfolded algorithm for super-resolution
Deep Equilibrium (DEQ) algorithms for image deblurring
Learned Primal-Dual algorithm for CT scan.