Skip to main content
Back to top
Ctrl
+
K
Quickstart
Examples
User Guide
API
Finding Help
More
Contributing to DeepInverse
Community
Change Log
Search
Ctrl
+
K
System Settings
Light
Dark
Search
Ctrl
+
K
Quickstart
Examples
User Guide
API
Finding Help
Contributing to DeepInverse
Community
Change Log
System Settings
Light
Dark
Collapse Sidebar
Expand Sidebar
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
Physics
Tour of forward sensing operators
Tour of blur operators
Tour of MRI functionality in DeepInverse
3D diffraction PSF
Ptychography phase retrieval
Random phase retrieval and reconstruction methods.
Inverse scattering problem
Single photon lidar operator for depth ranging.
Remote sensing with satellite images
Pattern Ordering in a Compressive Single Pixel Camera
Spatial unwrapping and modulo imaging
Optimization
3D 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
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.
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
Uncertainty quantification with PnP-ULA.
Using state-of-the-art diffusion models from HuggingFace Diffusers with DeepInverse
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
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 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
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
Radio interferometric imaging with deepinverse
Single-pixel imaging with Spyrit
Using HuggingFace datasets
Distributed Computing
Distributed Denoiser with Image Tiling
Distributed Physics Operators
Distributed Plug-and-Play (PnP) Reconstruction
Metrics
Fitting NIQE on a custom dataset
Examples
Metrics
Metrics
#
Fitting NIQE on a custom dataset
Fitting NIQE on a custom dataset
Show Source
