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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.
    • Stacking and concatenating forward operators.
    • Reconstructing an image using the deep image prior.
    • Creating your own dataset
    • Image transforms for equivariance & augmentations
    • Using huggingface dataset
    • Creating a forward operator.
    • Training a reconstruction network.
    • 3D diffraction PSF
    • 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.
  • 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 sampling algorithm.
    • Implementing DPS
    • 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 MRI reconstruction with Artifact2Artifact
    • 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 learning from incomplete measurements of multiple operators.
  • Adversarial Learning
    • Imaging inverse problems with adversarial networks
  • Advanced
    • Radio interferometric imaging with deepinverse
  • Examples
  • Patch Priors

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

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Unfolded Chambolle-Pock for constrained image inpainting

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Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting
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