Skip to main content

Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2505.20160 (eess)
[Submitted on 26 May 2025 (v1), last revised 17 Jun 2025 (this version, v2)]

Title:DeepInverse: A Python package for solving imaging inverse problems with deep learning

Authors:Julián Tachella, Matthieu Terris, Samuel Hurault, Andrew Wang, Dongdong Chen, Minh-Hai Nguyen, Maxime Song, Thomas Davies, Leo Davy, Jonathan Dong, Paul Escande, Johannes Hertrich, Zhiyuan Hu, Tobías I. Liaudat, Nils Laurent, Brett Levac, Mathurin Massias, Thomas Moreau, Thibaut Modrzyk, Brayan Monroy, Sebastian Neumayer, Jérémy Scanvic, Florian Sarron, Victor Sechaud, Georg Schramm, Romain Vo, Pierre Weiss
View PDF HTML (experimental)
Abstract:DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems. The library covers all crucial steps in image reconstruction from the efficient implementation of forward operators (e.g., optics, MRI, tomography), to the definition and resolution of variational problems and the design and training of advanced neural network architectures. In this paper, we describe the main functionality of the library and discuss the main design choices.
Comments: this https URL
Subjects: Image and Video Processing (eess.IV)
MSC classes: 65F22, 68T07
ACM classes: I.4.4; I.4.5; I.2.6
Cite as: arXiv:2505.20160 [eess.IV]
  (or arXiv:2505.20160v2 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2505.20160

Submission history

From: Matthieu Terris [view email]
[v1] Mon, 26 May 2025 16:04:17 UTC (3,789 KB)
[v2] Tue, 17 Jun 2025 12:45:51 UTC (3,789 KB)
Full-text links:

Access Paper:

  • View PDF
  • HTML (experimental)
  • TeX Source
view license

Current browse context:

eess.IV
< prev   |   next >
new | recent | 2025-05
Change to browse by:
eess

References & Citations

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)