GlobalBioIm - A unifying library for imaging inverse problems
Description: Our work in imaging inverse problems ranges from the theoretical exploration of the fundamentals of variational problems, to making them accessible to all engineers across imaging modalities[2, 3 & 4]. GlobalBioIm is a software library that integrates all our advances in this field and makes them easily available to the community. Imaging scientists in any biomedical imaging modality no longer need to reinvent and reimplement the forward models and optimisation algorithms at the core of the state-of-the-art reconstruction methods. This project is funded by the H2020 Advanced ERC Grant No 692726 (2016-2021).
Investigator: Michaël Unser (EPFL)
Collaborators: Shayan Aziznejad, Thomas Debarre, Laurène Donati, Julien Fageot, Harshit Gupta, Kyong Jin, Thanh-An Pham, Emmanuel Soubies, Ferréol Soulez, Luc Zeng, in direct collaboration with Michael T. McCann and Pol del Aguila Pla, funded by the CIBM.
References:  Michaël Unser ; Julien Fageot ; Harshit Gupta, Representer Theorems for Sparsity-Promoting ℓ1 Regularization, IEEE Transactions on Information Theory (Volume: 62 , Issue: 9 , Sept. 2016)
 Michael T. McCann and Michaël Unser, Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks, Foundations and Trends in Signal Processing (Vol. 13: No. 3, pp 283-359, December 2019)
 Emmanuel Soubies, Ferréol Soulez, Michael T McCann, Thanh-an Pham, Laurène Donati, Thomas Debarre, Daniel Sage and Michaël Unser, Pocket guide to solve inverse problems with GlobalBioIm, ( , , September 2019), Special Issue on Variational Methods and Effective Algorithms for Imaging and Vision
 Michaël Unser, Emmanuel Soubies, Ferréol Soulez, Michael McCann, and Laurène Donati, GlobalBioIm: A Unifying Computational Framework for Solving Inverse Problems, Optical Society of America (Paper CTu1B.1, 2017)