DS07 - Société de l'information et de la communication

Majorization-Minimization algorithms for Image Computing – MajIC

Submission summary

Recent developments in image processing brings the need for solving optimization problems with increasingly large sizes, pushing traditional techniques to their limits. New optimization algorithms need to be designed, paying attention to computational complexity, scalability, and robustness issues. Majorization-Minimization (MM) approaches have become increasingly popular recently, in both signal/image processing and machine learning fields. The MM framework provides simple, elegant and flexible ways to construct optimization algorithms that benefit from solid theoretical foundations and show great practical efficiency. The MAJIC project aims to propose a new generation of MM algorithms that remain efficient in the context of “big data” processing, thanks to the integration of parallel, distributed, and online computing strategies. Two challenging applications will be addressed: on-the-fly image reconstruction, and fast deep neural network learning.

Project coordination

Emilie Chouzenoux (Laboratoire d'Informatique Gaspard-Monge)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


LIGM Laboratoire d'Informatique Gaspard-Monge

Help of the ANR 190,080 euros
Beginning and duration of the scientific project: March 2018 - 48 Months

Useful links

Explorez notre base de projets financés



ANR makes available its datasets on funded projects, click here to find more.

Sign up for the latest news:
Subscribe to our newsletter