deepinv

FAIR level 5

This mature software has runnable examples and notebooks and should be easy to understand, run and deploy in your imaging pipeline.

Developed by

PyTorch library for solving imaging inverse problems using deep learning

Python

Visit the website

Description

PyTorch library for solving imaging inverse problems using deep learning

Date Created

2023-02-10T00:00:00.000Z

Date Updated

2023-07-01T00:00:00.000Z

Feature List

License

https://spdx.org/licenses/BSD-3-Clause.html

Citation DOI

https://zenodo.org/records/7982257

Related Images

README

https://github.com/deepinv/deepinv/blob/main/README.rst

Executable Instructions

https://deepinv.github.io/deepinv/quickstart.html

Executable Notebooks

Name	Description	Link
Random phase retrieval and reconstruction methods.	This example shows how to create a random phase retrieval operator and generate phaseless measurements from a given image.

Parameters

Name	Description	Format	Encoding format	Description	Dimensionality	Default value	Required
image	Images saved as torch tensors			Images saved as torch tensors	1		False

Example/Demo data

Name

Producers

Persons and Organizations are temporally plot on the same table. This will be divided in two tables.

Name	Affiliation	ORCID
Matthieu Terris	None	https://orcid.org/0009-0009-9726-6131
Andrew Wang	None	https://orcid.org/0000-0003-0838-7986
Julian Tachella	None	https://orcid.org/0000-0003-3878-9142
Dongdong Chen	None	https://orcid.org/0000-0002-7016-9288
Samuel Hurault	None	https://orcid.org/0000-0002-5163-2791

Citation

Maintainers

Name	Affiliation	ORCID
Julian Tachella	None	https://orcid.org/0000-0003-3878-9142
Dongdong Chen	None	https://orcid.org/0000-0002-7016-9288
Samuel Hurault	None	https://orcid.org/0000-0002-5163-2791
Matthieu Terris	None	https://orcid.org/0009-0009-9726-6131
Andrew Wang	None	https://orcid.org/0000-0003-0838-7986
Zhiyuan Hu	EPFL	https://orcid.org/0009-0001-5552-7833

A project by