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

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
    • Super-resolution with SRResNet
    • Reconstruct undersampled k-space for cardiac and brain MRI
    • Reconstruct prospectively-undersampled raw multicoil MRI
  • Physics
    • Tour of forward sensing operators
    • Tour of blur operators
    • Tour of MRI functionality in DeepInverse
    • Tour of ultrafast ultrasound in DeepInverse
    • 3D diffraction PSF
    • Random phase retrieval and reconstruction methods.
    • Spectral Methods for Non-Circular Deblurring with Liu-Jia Padding
    • 3D PET reconstruction with the Brainweb dataset
    • Positron emission tomography (PET) in 3D
    • Inverse scattering problem
    • Single photon lidar operator for depth ranging.
    • Ptychography phase retrieval
    • Positron emission tomography (PET) in 2D
    • Spatial unwrapping and modulo imaging
    • Remote sensing with satellite images
    • 2D PET reconstruction with the Brainweb dataset
    • Poisson-Gaussian Denoising with the Generalized Anscombe Transform
    • Pattern Ordering in a Compressive Single Pixel Camera
  • Optimization
    • 3D denoising of brain MRI with wavelet and TV priors
    • 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
    • Multispectral demosaicing from raw sensor data
    • Patch priors for limited-angle computed tomography
    • Poisson Inverse Problems with Maximum-Likelihood Expectation-Maximization (MLEM)
    • Reconstructing an image using the deep image prior.
  • Plug-and-Play
    • DPIR method for PnP image deblurring.
    • In-vivo ultrafast ultrasound reconstruction with Plug-and-Play
    • Multi-scale Plug-and-Play for Inpainting
    • Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.
    • PnP with custom optimization algorithm (Primal-Dual Condat-Vu)
    • 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
    • DPS – Posterior Sampling with Diffusion Models
    • Flow-Matching for posterior sampling and unconditional generation
    • Image reconstruction with a diffusion model
    • Implementing DiffPIR
    • Noisy data-fidelity terms for diffusion posterior sampling
    • Uncertainty quantification with PnP-ULA.
    • Using state-of-the-art diffusion models from HuggingFace Diffusers with DeepInverse
  • Unfolded
    • DEAL denoising and reconstruction
    • 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
    • Reducing the memory and computational complexity of unfolded network training
    • Unfolded Chambolle-Pock for constrained image inpainting
    • Vanilla Unfolded algorithm for super-resolution
  • Blind Inverse Problems
    • Blind Richardson-Lucy deconvolution
    • Blind deblurring with kernel estimation network
    • Blind denoising with noise level estimation
    • Calibrating physics operators
  • Self-Supervised Learning
    • Image transformations for Equivariant Imaging
    • Low-field MRI denoising without ground truth
    • Poisson denoising using Poisson2Sparse
    • Scan-specific zero-shot SSDU for MRI
    • 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 Equivariant Splitting
    • Self-supervised learning with measurement splitting
    • Ultrasound despeckling from B-mode images
  • Transformations & Equivariance
    • Image transforms for equivariance & augmentations
  • Adversarial Learning
    • Imaging inverse problems with adversarial networks
  • External Libraries
    • Loading scientific images
    • Low-dose CT with ASTRA backend and Total-Variation (TV) prior
    • Low-intensity STED fluorescence microscopy denoising
    • Radio interferometric imaging with deepinverse
    • Reconstruct accelerated non-Cartesian breast MRI acquisition data
    • Reconstruct real CT sinograms with the 2DeteCT benchmark
    • Single-pixel imaging with Spyrit
    • Using HuggingFace datasets
  • Distributed Computing
    • Distributed Denoiser with Image Tiling
    • Distributed Physics Operators
    • Distributed Plug-and-Play (PnP) Reconstruction
    • Distributed Training of Unfolded Networks
  • Metrics
    • Blind inverse problems with no reference metrics
    • Fitting NIQE on a custom dataset
  • Examples
  • Blind Inverse Problems

Blind Inverse Problems#

Blind Richardson-Lucy deconvolution

Blind Richardson-Lucy deconvolution

Blind deblurring with kernel estimation network

Blind deblurring with kernel estimation network

Blind denoising with noise level estimation

Blind denoising with noise level estimation

Calibrating physics operators

Calibrating physics operators

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Vanilla Unfolded algorithm for super-resolution

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Blind Richardson-Lucy deconvolution
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