About me

I am researcher in applied mathematics. My research interests include inverse problems, deep generative models and computer vision. I am currently a postdoctoral researcher at the research center on computer science, signal processing and automatics of Lille (CRIStAL). Before that, I have defended my PhD thesis on image restoration with deep generative models, in Bordeaux university, under the supervision of Nicolas Papadakis and Andrés Almansa.

News

• [23/04/2024] I will be presenting my work on inverse problem resolution with variational autoencoders at the 24th Congrès d analyse numérique (CANUM), on monday 27th of May.
• [10/12/2023] Our paper Efficient posterior sampling for diverse super-resolution with hierarchical VAE Prior has been accepted as an oral at the 19th International Joint Conference on Computer Vision Theory and Applications (VISAPP2024). preprint
• [15/11/2023] I have defended my PhD thesis Image restoration with deep generative models. The manuscript is available on HAL
• [14/07/2023] Our paper Inverse problem regularization with hierarchical variational autoencoders has been accepted as a poster in the main track of ICCV 2023!

Publications and preprints

• Efficient posterior sampling for diverse super-resolution with hierarchical VAE Prior
  Jean Prost, Antoine Houdard, Nicolas Papadakis, Andrés Almansa
  19th International Joint Conference on Computer Vision Theory and Applications (VISAPP2024) [arxiv]
• Plug-and-Play image restoration with Stochastic deNOising REgularization
  Marien Renaud, Jean Prost, Arthur Leclaire, Nicolas Papadakis
  (preprint) [arxiv] [code]
• Inverse problem regularization with hierarchical variational autoencoders
  Jean Prost, Antoine Houdard, Nicolas Papadakis, Andrés Almansa
  International Conference on Computer Vision (ICCV 2023) [arxiv] [paper] [code]
• SCOTCH and SODA: A Transformer Video Shadow Detection Framework
  Lihao Liu, Jean Prost, Lei Zhu, Nicolas Papadakis, Pietro Lio, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
  Computer Vision and Pattern Recognition (CVPR 2023) [arxiv] [paper] [code]
• Learning Local Regularization for Variational Image Restoration
  Jean Prost, Antoine Houdard, Andrés Almansa, Nicolas Papadakis
  Scale Space and Variational Methods in Computer Vision: 8th International Conference, (SSVM 2021) [arxiv] [paper] [code]

Talks

• Mathematical Models for Plug-and-play Image Restoration
  08/12/2022, Paris
  Diverse image super-resolution with hierarchical variational autoencoders
• Cambridge Image Analysis seminar
  02/09/2022, Cambridge
  Diverse image super-resolution with hierarchical variational autoencoders
• Generative models: Control and (mis)Usage
  31/05/2022, CNRS Ile-de-France Villejuif
  Diverse image super-resolution with hierarchical variational autoencoders
• SIAM Conference on Imaging Science (IS22)
  22/03/2022, Virtual conference
  Learning local regularization for variational image restoration
• ORASIS 2021
  17/09/2021, Saint-Ferréol
  Apprentissage d'une fonction de régularisation locale pour la restauration d'images"