Convex Formulation for Total-Variation Parameter Learning
07 Mar 2024, 15:00 — Room 322 @ DIBRIS/DIMA, Via Dodecaneso 35, Genoa
![Enis.Chenchene - [Fronte Capelli Naso]](/media/images/Enis_G7Tj0FX.width-800.jpg)
Speaker:
Enis Chenchene
Enis Chenchene
Abstract:
We present a new approach for data-driven tuning of regularization parameters for total-variation denoising. The proposed approach hinges on a specific proxy for the underlying bilevel problem, which admits a convex monolevel reformulation that can be efficiently solved with a new conditional-gradient-type method. We show numerical experiments and open avenues for promising extensions.
We present a new approach for data-driven tuning of regularization parameters for total-variation denoising. The proposed approach hinges on a specific proxy for the underlying bilevel problem, which admits a convex monolevel reformulation that can be efficiently solved with a new conditional-gradient-type method. We show numerical experiments and open avenues for promising extensions.
Bio:
Enis is currently a university assistant at the University of Graz. His primary research interests lie in splitting methods in convex optimization, optimal transport, and image processing. Before his current role, Enis earned his PhD in convex optimization at the University of Graz and worked on optimal transport in collaboration with MOKAPLAN in Paris.
Enis is currently a university assistant at the University of Graz. His primary research interests lie in splitting methods in convex optimization, optimal transport, and image processing. Before his current role, Enis earned his PhD in convex optimization at the University of Graz and worked on optimal transport in collaboration with MOKAPLAN in Paris.