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).
Basics
Single photon lidar operator for depth ranging.
Stacking and concatenating forward operators.
Reconstructing an image using the deep image prior.
A tour of forward sensing operators
Training a reconstruction network.
Image deblurring with custom deep explicit prior.
Optimization
Image deblurring with Total-Variation (TV) prior
Image inpainting with wavelet prior
Patch Priors
Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting
Patch priors for limited-angle computed tomography
Plug-and-Play
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.
Self-Supervised Learning
Self-supervised denoising with the SURE 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.
Unfolded
Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing
Vanilla Unfolded algorithm for super-resolution
Deep Equilibrium (DEQ) algorithms for image deblurring
Learned iterative custom prior
Learned Primal-Dual algorithm for CT scan.
Unfolded Chambolle-Pock for constrained image inpainting