Bilevel approaches for learning of variational imaging models
Abstract
We review some recent learning approaches in variational imaging, based on bilevel optimisation, and emphasize the importance of their treatment in function space. The paper covers both analytical and numerical techniques. Analytically, we include results on the existence and structure of minimisers, as well as optimality conditions for their characterisation. Based on this information, Newton type methods are studied for the solution of the problems at hand, combining them with sampling techniques in case of large databases. The computational verification of the developed techniques is extensively documented, covering instances with different type of regularisers, several noise models, spatially dependent weights and large image databases.
Cite
@article{arxiv.1505.02120,
title = {Bilevel approaches for learning of variational imaging models},
author = {Luca Calatroni and Cao Chung and Juan Carlos De Los Reyes and Carola-Bibiane Schönlieb and Tuomo Valkonen},
journal= {arXiv preprint arXiv:1505.02120},
year = {2016}
}