Reconstructor#

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

Bases: Module

Base class for reconstruction models.

Provides a template for defining reconstruction models.

Reconstructors provide a signal estimate x_hat as x_hat = model(y, physics) where y are the measurements and physics is the forward model \(A\) (possibly including information about the noise distribution too).

The base class inherits from torch.nn.Module.

forward(y, physics, **kwargs)[source]#

Applies reconstruction model \(\inversef{y}{A}\).

Parameters:
Returns:

(torch.Tensor) reconstructed tensor.

Examples using Reconstructor:#

Imaging inverse problems with adversarial networks

Imaging inverse problems with adversarial networks

Bring your own dataset

Bring your own dataset

Use iterative reconstruction algorithms

Use iterative reconstruction algorithms

Use a pretrained model

Use a pretrained model

5 minute quickstart tutorial

5 minute quickstart tutorial

Inference and fine-tune a foundation model

Inference and fine-tune a foundation model

Training a reconstruction model

Training a reconstruction model

Reconstructing an image using the deep image prior.

Reconstructing an image using the deep image prior.

Tour of MRI functionality in DeepInverse

Tour of MRI functionality in DeepInverse

Image reconstruction with a diffusion model

Image reconstruction with a diffusion model

Building your diffusion posterior sampling method using SDEs

Building your diffusion posterior sampling method using SDEs

Self-supervised MRI reconstruction with Artifact2Artifact

Self-supervised MRI reconstruction with Artifact2Artifact

Self-supervised learning with Equivariant Imaging for MRI.

Self-supervised learning with Equivariant Imaging for MRI.

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.