Year: 2025
Type: software-paper
Abstract: DeepInverse is an open-source PyTorch-based library for imaging inverse problems.DeepInverse implements all steps for image reconstruction, including efficient forward operators, defining and solving variational problems and designing and training advanced neural networks, for a wide set of domains (medical imaging, astronomical imaging, remote sensing, computational photography, compressed sensing and more).
DOI: 10.21105/joss.08923
Authors: Julián Tachella, Matthieu Terris, Samuel Hurault, Andrew Wang, Leo Davy, Jérémy Scanvic, Victor Sechaud, Romain Vo, Thomas Moreau, Thomas Davies, Dongdong Chen, Nils Laurent, Brayan Monroy, Jonathan Dong, Zhiyuan Hu, Minh-Hai Nguyen, Florian Sarron, Pierre Weiss, Paul Escande, Mathurin Massias, Thibaut Modrzyk, Brett Levac, Tobías I. Liaudat, Maxime Song, Johannes Hertrich, Sebastian Neumayer, Georg Schramm
Institutions: École Normale Supérieure de Lyon, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, École Normale Supérieure - PSL, University of Edinburgh, Heriot-Watt University, Universidad Industrial de Santander, École Polytechnique Fédérale de Lausanne, Institut de Recherche en Informatique de Toulouse, Université de Toulouse, Centre de Biologie Intégrative de Toulouse, Institut National des Sciences Appliquées de Lyon, The University of Texas at Austin, Institut de Recherche sur les Lois Fondamentales de l'Univers, Institut du développement et des ressources en informatique scientifique, Université Paris Dauphine-PSL, Chemnitz University of Technology, KU Leuven
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funders: National Science Foundation, Heriot-Watt University, UK Research and Innovation, Deutsche Forschungsgemeinschaft, Agence Nationale de la Recherche, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Centre National de la Recherche Scientifique, Grand Équipement National De Calcul Intensif, Engineering and Physical Sciences Research Council
Awards: Bayesian computation for low-photon imaging, Blind inverse problems and optical microscopy, Variationelle Gradientenflüsse auf Wahrscheinlichkeitsräumen und Generative Modelle für Bayessche Inverse Probleme in der Bildverarbeitung, Bayesian computation for low-photon imaging, Harnessing randomness for single-shot quantitative phase imaging, Bridging Artificial Intelligence and Neuroscience, G5661380953, Data-oriented Software and Tools for the Exascale, Unsupervised Learning for Non-Linear Inverse Problems
OpenAlex ID: W4416378186