Denoiser

class deepinv.models.Denoiser(device='cpu')

Bases: Module

Base class for denoiser models.

Provides a template for defining denoiser models.

While most denoisers \(\denoisername\) are designed to handle Gaussian noise with variance \(\sigma^2\), this is not mandatory.

Note

A Denoiser can be converted into a Reconstructor by using the deepinv.models.ArtifactRemoval class.

The base class inherits from torch.nn.Module.

forward(x, sigma, **kwargs)

Applies denoiser \(\denoiser{x}{\sigma}\).

Parameters:

• x (torch.Tensor) – noisy input.

• sigma (torch.Tensor, float) – noise level.

Returns:

(torch.Tensor) Denoised tensor.

Examples using Denoiser:

Imaging inverse problems with adversarial networks

Imaging inverse problems with adversarial networks

Saving and loading models

Saving and loading models

Random phase retrieval and reconstruction methods.

Random phase retrieval and reconstruction methods.

Tour of MRI functionality in DeepInverse

Tour of MRI functionality in DeepInverse

Training a reconstruction network.

Training a reconstruction network.

Image transforms for equivariance & augmentations

Image transforms for equivariance & augmentations

3D wavelet denoising

3D wavelet denoising

DPIR method for PnP image deblurring.

DPIR method for PnP image deblurring.

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

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

Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.

Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.

Vanilla PnP for computed tomography (CT).

Vanilla PnP for computed tomography (CT).

Building your custom MCMC sampling algorithm.

Building your custom MCMC sampling algorithm.

Image reconstruction with a diffusion model

Image reconstruction with a diffusion model

Implementing DiffPIR

Implementing DiffPIR

Building your diffusion posterior sampling method using SDEs

Building your diffusion posterior sampling method using SDEs

Implementing DPS

Implementing DPS

Uncertainty quantification with PnP-ULA.

Uncertainty quantification with PnP-ULA.

Image transformations for Equivariant Imaging

Image transformations for Equivariant Imaging

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised denoising with the Neighbor2Neighbor loss.

Self-supervised denoising with the Neighbor2Neighbor loss.

Self-supervised denoising with the Generalized R2R loss.

Self-supervised denoising with the Generalized R2R loss.

Self-supervised learning with measurement splitting

Self-supervised learning with measurement splitting

Self-supervised denoising with the SURE loss.

Self-supervised denoising with the SURE loss.

Self-supervised denoising with the UNSURE loss.

Self-supervised denoising with the UNSURE loss.

Deep Equilibrium (DEQ) algorithms for image deblurring

Deep Equilibrium (DEQ) algorithms for image deblurring

Unfolded Chambolle-Pock for constrained image inpainting

Unfolded Chambolle-Pock for constrained image inpainting

Vanilla Unfolded algorithm for super-resolution

Vanilla Unfolded algorithm for super-resolution