4 citations · 9 across the 7 of their papers we have counts for
4 papers · 1 filter
Learning Regularization Functionals for Inverse Problems: A Comparative Study
Johannes Hertrich, Hok Shing Wong, Alexander Denker +16
In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insig…
Breaking the Weak Recovery Limit in Random Phase Retrieval with Learned Regularizers
Stanislas Ducotterd, Zhiyuan Hu, Michael Unser +1
We seek to recover an unknown signal from nonlinear amplitude-only measurements, a challenging inverse problem. Strong theoretical guarantees have been established for idealized ra…
Multivariate Fields of Experts for Convergent Image Reconstruction
Stanislas Ducotterd, Michael Unser
We introduce the multivariate fields of experts, a new framework for the learning of image priors. Our model generalizes existing fields of experts methods by incorporating multiva…
Universal Architectures for the Learning of Polyhedral Norms and Convex Regularizers
Michael Unser, Stanislas Ducotterd
This paper addresses the task of learning convex regularizers to guide the reconstruction of images from limited data. By imposing that the reconstruction be amplitude-equivariant,…