Examples

All the examples have a download link at the end. You can load the example’s notebook on Google Colab and run them by adding the line

pip install git+https://github.com/deepinv/deepinv.git#egg=deepinv

to the top of the notebook (e.g., as in here).

Basics

Single photon lidar operator for depth ranging.

Single photon lidar operator for depth ranging.

Reconstructing an image using the deep image prior.

Reconstructing an image using the deep image prior.

Creating your own dataset

Creating your own dataset

Image transforms for equivariance & augmentations

Image transforms for equivariance & augmentations

Using huggingface dataset

Using huggingface dataset

Ptychography phase retrieval

Ptychography phase retrieval

Creating a forward operator.

Creating a forward operator.

Remote sensing with satellite images

Remote sensing with satellite images

3D diffraction PSF

3D diffraction PSF

Training a reconstruction network.

Training a reconstruction network.

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.

Saving and loading models

Saving and loading models

A tour of blur operators

A tour of blur operators

Random phase retrieval and reconstruction methods.

Random phase retrieval and reconstruction methods.

Tour of MRI functionality in DeepInverse

Tour of MRI functionality in DeepInverse

Optimization

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

Plug-and-Play

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.

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)

Sampling

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

Unfolded

Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing

Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing

Vanilla Unfolded algorithm for super-resolution

Vanilla Unfolded algorithm for super-resolution

Learned iterative custom prior

Learned iterative custom prior

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.

Unfolded Chambolle-Pock for constrained image inpainting

Unfolded Chambolle-Pock for constrained image inpainting

Patch Priors

Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting

Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting

Patch priors for limited-angle computed tomography

Patch priors for limited-angle computed tomography

Self-Supervised Learning

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 with Equivariant Imaging for MRI.

Self-supervised learning with Equivariant Imaging for MRI.

Self-supervised denoising with the Generalized R2R loss.

Self-supervised denoising with the Generalized R2R loss.

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised MRI reconstruction with Artifact2Artifact

Self-supervised MRI reconstruction with Artifact2Artifact

Adversarial Learning

Imaging inverse problems with adversarial networks

Imaging inverse problems with adversarial networks

Advanced

Radio interferometric imaging with deepinverse

Radio interferometric imaging with deepinverse