Improving image reconstruction algorithms with model mismatches
par
Pierre Weiss(IMT)
→
Europe/Paris
Salle K. Johnson (IMT)
Salle K. Johnson
IMT
Description
In this talk, I will describe an overview of our recent attempts to improve imaging systems using deep learning techniques. While efficient tools are now available when the forward model describing the acquisition device is well calibrated, they usually perform terribly when model mismatches occur. In that case, we will see that specific neural network architectures make it possible to identify the system from the measurements themselves. This yields self-calibrated reconstruction algorithms. Application examples include optical imaging, MRI or computerized tomography.
This talk will also be an opportunity to discuss potential interactions with the CBI (Centre de Biologie Intégrative), where I just created a group. Many biologists produce complex data sets and are expressing a strong will to discuss modelling issues.