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  • Quickstart
  • Examples
  • User Guide
  • API
  • Finding Help
  • How to Contribute
  • Community

Section Navigation

  • Basics
    • Single photon lidar operator for depth ranging.
    • Reconstructing an image using the deep image prior.
    • Creating your own dataset
    • Image transforms for equivariance & augmentations
    • Using huggingface dataset
    • Ptychography phase retrieval
    • Creating a forward operator.
    • Remote sensing with satellite images
    • 3D diffraction PSF
    • Training a reconstruction network.
    • A tour of forward sensing operators
    • Image deblurring with custom deep explicit prior.
    • Saving and loading models
    • A tour of blur operators
    • Random phase retrieval and reconstruction methods.
    • Tour of MRI functionality in DeepInverse
    • A tour of DeepInv’s denoisers
  • Optimization
    • Image deblurring with Total-Variation (TV) prior
    • Image inpainting with wavelet prior
    • 3D wavelet denoising
  • Plug-and-Play
    • Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.
    • Vanilla PnP for computed tomography (CT).
    • DPIR method for PnP image deblurring.
    • Regularization by Denoising (RED) for Super-Resolution.
    • PnP with custom optimization algorithm (Condat-Vu Primal-Dual)
  • Sampling
    • Uncertainty quantification with PnP-ULA.
    • Image reconstruction with a diffusion model
    • Building your custom MCMC sampling algorithm.
    • Implementing DPS
    • Building your diffusion posterior sampling method using SDEs
    • Implementing DiffPIR
  • Unfolded
    • Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing
    • Vanilla Unfolded algorithm for super-resolution
    • Learned iterative custom prior
    • Deep Equilibrium (DEQ) algorithms for image deblurring
    • Learned Primal-Dual algorithm for CT scan.
    • Unfolded Chambolle-Pock for constrained image inpainting
  • Patch Priors
    • Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting
    • Patch priors for limited-angle computed tomography
  • Self-Supervised Learning
    • Image transformations for Equivariant Imaging
    • Self-supervised learning with measurement splitting
    • Self-supervised denoising with the UNSURE loss.
    • Self-supervised denoising with the SURE loss.
    • Self-supervised denoising with the Neighbor2Neighbor loss.
    • Self-supervised learning with Equivariant Imaging for MRI.
    • Self-supervised denoising with the Generalized R2R loss.
    • Self-supervised learning from incomplete measurements of multiple operators.
    • Self-supervised MRI reconstruction with Artifact2Artifact
  • Adversarial Learning
    • Imaging inverse problems with adversarial networks
  • Advanced
    • Radio interferometric imaging with deepinverse
  • Examples
  • Sampling

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 MCMC sampling algorithm.

Building your custom MCMC sampling algorithm.

Implementing DPS

Implementing DPS

Building your diffusion posterior sampling method using SDEs

Building your diffusion posterior sampling method using SDEs

Implementing DiffPIR

Implementing DiffPIR

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

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Uncertainty quantification with PnP-ULA.

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