Robin Kenis*

Convex Relaxations for Manifold-Valued Markov Random Fields with Approximation Guarantees

"While neural network models have garnered significant attention in the imaging community, their application remains limited in important settings where optimality certificates are required or in the absence of extensive datasets. In such cases, classical models like (continuous) Markov Random Field…