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).

Get started#

Get started with the 5 minute quickstart tutorial, or view it in Colab here.

Basics#

5 minute quickstart tutorial

5 minute quickstart tutorial

Use a pretrained model

Use a pretrained model

Use iterative reconstruction algorithms

Use iterative reconstruction algorithms

Bring your own dataset

Bring your own dataset

Bring your own physics

Bring your own physics

Models#

Inference and fine-tune a foundation model

Inference and fine-tune a foundation model

Training a reconstruction model

Training a reconstruction model

Benchmarking pretrained denoisers

Benchmarking pretrained denoisers

Physics#

Tour of forward sensing operators

Tour of forward sensing operators

Tour of blur operators

Tour of blur operators

Tour of MRI functionality in DeepInverse

Tour of MRI functionality in DeepInverse

Pattern Ordering in a Compressive Single Pixel Camera

Pattern Ordering in a Compressive Single Pixel Camera

Remote sensing with satellite images

Remote sensing with satellite images

Random phase retrieval and reconstruction methods.

Random phase retrieval and reconstruction methods.

Single photon lidar operator for depth ranging.

Single photon lidar operator for depth ranging.

Ptychography phase retrieval

Ptychography phase retrieval

3D diffraction PSF

3D diffraction PSF

Solving blind inverse problems / estimating physics parameters

Solving blind inverse problems / estimating physics parameters

Optimization#

3D wavelet denoising

3D wavelet denoising

Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting

Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting

Image deblurring with Total-Variation (TV) prior

Image deblurring with Total-Variation (TV) prior

Image deblurring with custom deep explicit prior.

Image deblurring with custom deep explicit prior.

Image inpainting with wavelet prior

Image inpainting with wavelet prior

Patch priors for limited-angle computed tomography

Patch priors for limited-angle computed tomography

Reconstructing an image using the deep image prior.

Reconstructing an image using the deep image prior.

Plug-and-Play#

DPIR method for PnP image deblurring.

DPIR method for PnP image deblurring.

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

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

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

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

Regularization by Denoising (RED) for Super-Resolution.

Regularization by Denoising (RED) for Super-Resolution.

Vanilla PnP for computed tomography (CT).

Vanilla PnP for computed tomography (CT).

Diffusion & MCMC#

Building your custom MCMC sampling algorithm.

Building your custom MCMC sampling algorithm.

Building your diffusion posterior sampling method using SDEs

Building your diffusion posterior sampling method using SDEs

Image reconstruction with a diffusion model

Image reconstruction with a diffusion model

Implementing DPS

Implementing DPS

Implementing DiffPIR

Implementing DiffPIR

Uncertainty quantification with PnP-ULA.

Uncertainty quantification with PnP-ULA.

Unfolded#

Deep Equilibrium (DEQ) algorithms for image deblurring

Deep Equilibrium (DEQ) algorithms for image deblurring

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

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

Learned Primal-Dual algorithm for CT scan.

Learned Primal-Dual algorithm for CT scan.

Learned iterative custom prior

Learned iterative custom prior

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

Self-Supervised Learning#

Image transformations for Equivariant Imaging

Image transformations for Equivariant Imaging

Image transforms for equivariance & augmentations

Image transforms for equivariance & augmentations

Self-supervised MRI reconstruction with Artifact2Artifact

Self-supervised MRI reconstruction with Artifact2Artifact

Self-supervised denoising with the Generalized R2R loss.

Self-supervised denoising with the Generalized R2R loss.

Self-supervised denoising with the Neighbor2Neighbor loss.

Self-supervised denoising with the Neighbor2Neighbor loss.

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.

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised learning with Equivariant Imaging for MRI.

Self-supervised learning with Equivariant Imaging for MRI.

Self-supervised learning with measurement splitting

Self-supervised learning with measurement splitting

Adversarial Learning#

Imaging inverse problems with adversarial networks

Imaging inverse problems with adversarial networks

External Libraries#

Low-dose CT with ASTRA backend and Total-Variation (TV) prior

Low-dose CT with ASTRA backend and Total-Variation (TV) prior

Radio interferometric imaging with deepinverse

Radio interferometric imaging with deepinverse

Using HuggingFace datasets

Using HuggingFace datasets

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