Skip to main content
Ctrl+K
📧 Join our mailing list for releases and updates.
deepinv 0.4.2 documentation - Home deepinv 0.4.2 documentation - Home
  • Quickstart
  • Examples
  • User Guide
  • API
  • Benchmarks
    • Finding Help
    • Contributing to DeepInverse
    • Community
    • Change Log
  • 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
  • Physics
    • Tour of forward sensing operators
    • Tour of blur operators
    • Tour of MRI functionality in DeepInverse
    • 3D diffraction PSF
    • Random phase retrieval and reconstruction methods.
    • Spectral Methods for Non-Circular Deblurring with Liu-Jia Padding
    • Ptychography phase retrieval
    • Inverse scattering problem
    • Poisson-Gaussian Denoising with the Generalized Anscombe Transform
    • Pattern Ordering in a Compressive Single Pixel Camera
    • Spatial unwrapping and modulo imaging
    • Positron emission tomography (PET) in 3D
    • Single photon lidar operator for depth ranging.
    • Positron emission tomography (PET) in 2D
    • Remote sensing with satellite images
  • 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
    • 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.
    • 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
    • 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 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
    • 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
  • Metrics

Metrics#

Blind inverse problems with no reference metrics

Blind inverse problems with no reference metrics

Fitting NIQE on a custom dataset

Fitting NIQE on a custom dataset

previous

Distributed Training of Unfolded Networks

next

Blind inverse problems with no reference metrics
Show Source

© Copyright deepinverse contributors 2026.

Created using Sphinx 9.1.0.

Built with the PyData Sphinx Theme 0.21.0.