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  • Quickstart
  • Examples
  • User Guide
  • API
  • Finding Help
  • Contributing to DeepInverse
  • Community

Section Navigation

  • Basics
    • 5 minute quickstart tutorial
    • Use a pretrained model
    • Use iterative reconstruction algorithms
    • Bring your own dataset
    • Bring your own physics
  • Models
    • Inference and fine-tune a foundation model
    • Training a reconstruction model
    • Benchmarking pretrained denoisers
  • Physics
    • Tour of forward sensing operators
    • Tour of blur operators
    • Tour of MRI functionality in DeepInverse
    • Pattern Ordering in a Compressive Single Pixel Camera
    • Remote sensing with satellite images
    • Random phase retrieval and reconstruction methods.
    • Single photon lidar operator for depth ranging.
    • Ptychography phase retrieval
    • 3D diffraction PSF
    • Solving blind inverse problems / estimating physics parameters
  • Optimization
    • 3D wavelet denoising
    • Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting
    • Image deblurring with Total-Variation (TV) prior
    • Image deblurring with custom deep explicit prior.
    • Image inpainting with wavelet prior
    • Patch priors for limited-angle computed tomography
    • Reconstructing an image using the deep image prior.
  • Plug-and-Play
    • DPIR method for PnP image deblurring.
    • Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.
    • PnP with custom optimization algorithm (Condat-Vu Primal-Dual)
    • Regularization by Denoising (RED) for Super-Resolution.
    • Vanilla PnP for computed tomography (CT).
  • Diffusion & MCMC
    • Building your custom MCMC sampling algorithm.
    • Building your diffusion posterior sampling method using SDEs
    • Image reconstruction with a diffusion model
    • Implementing DPS
    • Implementing DiffPIR
    • Uncertainty quantification with PnP-ULA.
  • Unfolded
    • Deep Equilibrium (DEQ) algorithms for image deblurring
    • Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing
    • Learned Primal-Dual algorithm for CT scan.
    • Learned iterative custom prior
    • Unfolded Chambolle-Pock for constrained image inpainting
    • Vanilla Unfolded algorithm for super-resolution
  • Self-Supervised Learning
    • Image transformations for Equivariant Imaging
    • Image transforms for equivariance & augmentations
    • Self-supervised MRI reconstruction with Artifact2Artifact
    • Self-supervised denoising with the Generalized R2R loss.
    • Self-supervised denoising with the Neighbor2Neighbor loss.
    • Self-supervised denoising with the SURE loss.
    • Self-supervised denoising with the UNSURE loss.
    • Self-supervised learning from incomplete measurements of multiple operators.
    • Self-supervised learning with Equivariant Imaging for MRI.
    • Self-supervised learning with measurement splitting
  • Adversarial Learning
    • Imaging inverse problems with adversarial networks
  • External Libraries
    • Low-dose CT with ASTRA backend and Total-Variation (TV) prior
    • Radio interferometric imaging with deepinverse
    • Using HuggingFace datasets
  • Examples
  • Diffusion & MCMC

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.

previous

Vanilla PnP for computed tomography (CT).

next

Building your custom MCMC sampling algorithm.

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