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https://github.com/deepinv/deepinv

PyTorch library for solving imaging inverse problems using deep learning
https://github.com/deepinv/deepinv

computational-imaging computed-tomography deblurring deep-equilibrium-models deep-learning diffusion-models image-processing inverse-problems mri plug-and-play super-resolution unfolded

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PyTorch library for solving imaging inverse problems using deep learning

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README 

        
.. image:: https://github.com/deepinv/deepinv/raw/main/docs/source/figures/deepinv_logolarge.png

   :width: 500px

   :alt: deepinv logo

   :align: center



|Test Status| |Docs Status| |Python Version| |Black| |codecov| |discord| |colab|



Introduction

------------

DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems using deep learning. The goal of ``deepinv`` is to accelerate the development of deep learning based methods for imaging inverse problems, by combining popular learning-based reconstruction approaches in a common and simplified framework, standardizing forward imaging models and simplifying the creation of imaging datasets.



``deepinv`` features



* A 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 `_

* A framework for `building datasets `_ for inverse problems

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

* `Sampling algorithms `_ for uncertainty quantification (Langevin, diffusion, etc.)

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



.. image:: https://github.com/deepinv/deepinv/raw/main/docs/source/figures/deepinv_schematic.png

   :width: 1000px

   :alt: deepinv schematic

   :align: center



Documentation

-------------



Read the documentation and examples at `https://deepinv.github.io `_.



Install

-------



To install the latest stable release of ``deepinv``, you can simply do:



.. code-block:: bash



    pip install deepinv



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



.. code-block:: bash



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



You can also install additional dependencies needed for some modules in deepinv.datasets and deepinv.models:



.. code-block:: bash



    pip install deepinv[dataset,denoisers]



    # or



    pip install git+https://github.com/deepinv/deepinv.git#egg=deepinv[dataset,denoisers]



Quickstart

----------

Try out the following plug-and-play image inpainting example:



.. code-block:: python



   import deepinv as dinv

   from deepinv.utils import load_url_image



   url = ("https://huggingface.co/datasets/deepinv/images/resolve/main/cameraman.png?download=true")

   x = load_url_image(url=url, img_size=512, grayscale=True, device='cpu')



   physics = dinv.physics.Inpainting((1, 512, 512), mask = 0.5, \

                                       noise_model=dinv.physics.GaussianNoise(sigma=0.01))



   data_fidelity = dinv.optim.data_fidelity.L2()

   prior = dinv.optim.prior.PnP(denoiser=dinv.models.MedianFilter())

   model = dinv.optim.optim_builder(iteration="HQS", prior=prior, data_fidelity=data_fidelity, \

                                    params_algo={"stepsize": 1.0, "g_param": 0.1})

   y = physics(x)

   x_hat = model(y, physics)

   dinv.utils.plot([x, y, x_hat], ["signal", "measurement", "estimate"], rescale_mode='clip')



Also try out `one of the examples `_ to get started or check out our comprehensive `User Guide `_.



Contributing

------------



DeepInverse is a community-driven project and welcomes contributions of all forms.

We are ultimately aiming for a comprehensive library of inverse problems and deep learning,

and we need your help to get there!

The preferred way to contribute to ``deepinv`` is to fork the `main

repository `_ on GitHub,

then submit a "Pull Request" (PR). See our `contributing guide `_

for more details.



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 `_.



.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg

    :target: https://github.com/psf/black

.. |Test Status| image:: https://github.com/deepinv/deepinv/actions/workflows/test.yml/badge.svg

   :target: https://github.com/deepinv/deepinv/actions/workflows/test.yml

.. |Docs Status| image:: https://github.com/deepinv/deepinv/actions/workflows/documentation.yml/badge.svg

   :target: https://github.com/deepinv/deepinv/actions/workflows/documentation.yml

.. |Python Version| image:: https://img.shields.io/badge/python-3.9%2B-blue

   :target: https://www.python.org/downloads/release/python-390/

.. |codecov| image:: https://codecov.io/gh/deepinv/deepinv/branch/main/graph/badge.svg?token=77JRvUhQzh

   :target: https://codecov.io/gh/deepinv/deepinv

.. |discord| image:: https://dcbadge.vercel.app/api/server/qBqY5jKw3p?style=flat

   :target: https://discord.gg/qBqY5jKw3p

.. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg

   :target: https://colab.research.google.com/drive/1XhCO5S1dYN3eKm4NEkczzVU7ZLBuE42J