2 citations · 4 across the 9 of their papers we have counts for
11 papers
Guidance Without Labels: Flow Matching from Coupled Gaussian Mixtures
Stanislas Ducotterd, Bassam El Rawas, Michael Unser
Flow matching models are typically trained to transport a Gaussian source to the data distribution, with source and target samples paired independently. We replace the source with…
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…
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,…
DEALing with Image Reconstruction: Deep Attentive Least Squares
Mehrsa Pourya, Erich Kobler, Michael Unser +1
State-of-the-art image reconstruction often relies on complex, highly parameterized deep architectures. We propose an alternative: a data-driven reconstruction method inspired by t…
Comparison of 2D Regular Lattices for the CPWL Approximation of Functions
Mehrsa Pourya, Maïka Nogarotto, Michael Unser
We investigate the approximation error of functions with continuous and piecewise-linear (CPWL) representations. We focus on the CPWL search spaces generated by translates of box s…
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…
Parseval Convolution Operators and Neural Networks
Michael Unser, Stanislas Ducotterd
We first establish a kernel theorem that characterizes all linear shift-invariant (LSI) operators acting on discrete multicomponent signals. This result naturally leads to the iden…
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…
Iteratively Refined Image Reconstruction with Learned Attentive Regularizers
Mehrsa Pourya, Sebastian Neumayer, Michael Unser
We propose a regularization scheme for image reconstruction that leverages the power of deep learning while hinging on classic sparsity-promoting models. Many deep-learning-based m…
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…