8 papers
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…
Flower: A Flow-Matching Solver for Inverse Problems
Mehrsa Pourya, Bassam El Rawas, Michael Unser
We introduce Flower, a solver for linear inverse problems. It leverages a pre-trained flow model to produce reconstructions that are consistent with the observed measurements. Flow…
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,…
Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising
Sébastien Herbreteau, Michael Unser
Supervised deep learning has become the method of choice for image denoising. It involves the training of neural networks on large datasets composed of pairs of noisy and clean ima…
Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures
Michael Unser, Alexis Goujon, Stanislas Ducotterd
We present a general variational framework for the training of freeform nonlinearities in layered computational architectures subject to some slope constraints. The regularization…
Learning of Patch-Based Smooth-Plus-Sparse Models for Image Reconstruction
Stanislas Ducotterd, Sebastian Neumayer, Michael Unser
We aim at the solution of inverse problems in imaging, by combining a penalized sparse representation of image patches with an unconstrained smooth one. This allows for a straightf…