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Contents:

 * Physics
 * Datasets
 * Utils
 * Loss
 * Denoisers
 * Optim
 * Iterative Reconstruction (PnP, RED, etc.)
 * Unfolded Algorithms
 * Diffusion Algorithms
 * Other Reconstruction Methods
 * Examples
 * Using multiple GPUs
 * Math Notation
 * How to Contribute

deepinverse
 * 
 * DeepInverse: a Pytorch library for imaging with deep learning
 * View page source

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DEEPINVERSE: A PYTORCH LIBRARY FOR IMAGING WITH DEEP LEARNING



Deep Inverse is a Pytorch based library for solving imaging inverse problems
with deep learning.

Github repository: https://github.com/deepinv/deepinv.


FEATURING

 * Large collection of predefined imaging operators (MRI, CT, deblurring,
   inpainting, etc.)

 * Training losses for inverse problems (self-supervised learning,
   regularization, etc.).

 * Many pretrained deep denoisers which can be used for plug-and-play
   restoration.

 * Framework for building datasets for inverse problems.

 * Easy-to-build unfolded architectures (ADMM, forward-backward, deep
   equilibrium, etc.).

 * Diffusion algorithms for image restoration and uncertainty quantification
   (Langevin, diffusion, etc.).

 * A large number of well-explained examples, from basics to state-of-the-art
   methods.


INSTALLATION

Install the latest version of deepinv via pip:

pip install deepinv


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You can also install the latest version of deepinv directly from github:

pip install git+https://github.com/deepinv/deepinv.git#egg=deepinv


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GETTING STARTED

Try out one of the following deblurring examples (or pick from full list of
examples):

Image deblurring with custom deep explicit prior.

Image deblurring with custom deep explicit prior.

A tour of blur operators

A tour of blur operators

Image deblurring with Total-Variation (TV) prior

Image deblurring with Total-Variation (TV) prior

DPIR method for PnP image deblurring.

DPIR method for PnP image deblurring.

Building your custom sampling algorithm.

Building your custom sampling algorithm.

Implementing DiffPIR

Implementing DiffPIR

Deep Equilibrium (DEQ) algorithms for image deblurring

Deep Equilibrium (DEQ) algorithms for image deblurring



FINDING HELP

If you have any questions or suggestions, please join the conversation in our
Discord server. The recommended way to get in touch with the developers is to
open an issue on the issue tracker.


LEAD DEVELOPERS

Julian Tachella, Dongdong Chen, Samuel Hurault and Matthieu Terris.

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