Examples#
All the examples have a download link at the end. You can load the example’s notebook on Google Colab and run them by adding the line
pip install git+https://github.com/deepinv/deepinv.git#egg=deepinv
to the top of the notebook (e.g., as in here).
Get started#
Get started with the 5 minute quickstart tutorial, or view it in Colab here.
Basics#
Models#
Physics#
Pattern Ordering in a Compressive Single Pixel Camera
Solving blind inverse problems / estimating physics parameters
Random phase retrieval and reconstruction methods.
Optimization#
Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting
Patch priors for limited-angle computed tomography
Reconstructing an image using the deep image prior.
Plug-and-Play#
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.
Diffusion & MCMC#
Building your diffusion posterior sampling method using SDEs
Unfolded#
Deep Equilibrium (DEQ) algorithms for image deblurring
Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing
Reducing the memory and computational complexity of unfolded network training
Unfolded Chambolle-Pock for constrained image inpainting
Self-Supervised Learning#
Self-supervised MRI reconstruction with Artifact2Artifact
Self-supervised denoising with the Generalized R2R loss.
Self-supervised denoising with the Neighbor2Neighbor 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
