Regularization via deep generative models: an analysis point of view

June 25, 2021 — 11:00 — Location: Online


In this talk, we present a new way of regularizing an inverse problem in imaging (e.g., deblurring or inpainting) by means of a deep generative neural network. Compared to end-to-end models, such approaches seem particularly interesting since the same network can be used for many different problems and experimental conditions, as soon as the generative model is suited to the data. Previous works proposed to use a synthesis framework, where the estimation is performed on the latent vector, the solution being obtained afterwards via the decoder. Instead, we propose an analysis formulation where we directly optimize the image itself and penalize the latent vector. We illustrate the interest of such a formulation by running experiments of inpainting, deblurring and super-resolution. In many cases our technique achieves a clear improvement of the performance and seems to be more robust, in particular with respect to initialization.


Oberlin, T., & Verm, M. (2021). Regularization via deep generative models: an analysis point of view. To appear in ICIP 2021,


Thomas Oberlin holds a Ph.D. in applied mathematics from the University of Grenoble. In 2014, he was a postdoctoral fellow in signal processing and medical imaging at Inria Rennes, before joining as an Assistant Professor INP Toulouse - ENSEEIHT and the IRIT Laboratory, at Université de Toulouse. Since 2019, he is an Assistant/Associate Professor of Image Processing and Machine Learning at ISAE-SUPAERO and member of IA institute ANITI. His research interests include hyperspectral/spectral imaging, latent factor models, data-driven regularization of inverse problems, and time-frequency representations.