Denoiser#

class deepinv.models.Denoiser(device='cpu')[source]#

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)[source]#

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

Parameters:
Returns:

(torch.Tensor) Denoised tensor.

Examples using Denoiser:#

Image transforms for equivariance & augmentations

Image transforms for equivariance & augmentations

Training a reconstruction network.

Training a reconstruction network.

Saving and loading models

Saving and loading models

Random phase retrieval and reconstruction methods.

Random phase retrieval and reconstruction methods.

3D wavelet denoising

3D wavelet denoising

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).

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)

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 DPS

Implementing DPS

Implementing DiffPIR

Implementing DiffPIR

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

Unfolded Chambolle-Pock for constrained image inpainting

Unfolded Chambolle-Pock for constrained image inpainting

Image transformations for Equivariant Imaging

Image transformations for Equivariant Imaging

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.

Self-supervised denoising with the SURE loss.

Self-supervised denoising with the SURE loss.

Self-supervised denoising with the Neighbor2Neighbor loss.

Self-supervised denoising with the Neighbor2Neighbor loss.

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised learning from incomplete measurements of multiple operators.

Imaging inverse problems with adversarial networks

Imaging inverse problems with adversarial networks