Skip to content. | Skip to navigation

Personal tools


UMR 5672

logo de l'ENS de Lyon
logo du CNRS
You are here: Home / Seminars / Colloquium / Equivariant Imaging: learning to solve inverse problems without ground truth

Equivariant Imaging: learning to solve inverse problems without ground truth

Julian Tachella (LPENSL)
When Jan 10, 2022
from 11:00 to 12:00
Where Online
Attendees Julian Tachella
Add event to calendar vCal

In recent years, deep neural networks have obtained state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography. Networks are generally trained with pairs of signals and associated measurements. However, in various imaging problems, we usually only have access to compressed measurements of the underlying signals, thus hindering this learning-based approach. Learning from measurement data only is impossible in general, as the compressed observations do not contain information outside the range of the forward sensing operator. In this talk, I will present a new learning framework, called Equivariant Imaging, which overcomes this limitation by exploiting the invariance to transformations (translations, rotations, etc.) present in natural signals. I will also discuss necessary and sufficient conditions for learning without ground truth. Our proposed learning strategy performs as well as fully supervised methods and can handle noisy data. I will show results on various inverse problems, including sparse-view X-ray computed tomography, accelerated magnetic resonance imaging and image inpainting.

More information about this event…