2 citations · 2 across the 9 of their papers we have counts for
6 papers · 1 filter
Accelerated Sampling with the Inertial Langevin Algorithm
Alexander Falk, Andreas Habring, Christoph Griesbacher +1
We consider the \emph{inertial Langevin algorithm} (ILA), a simple momentum-based method for sampling from Gibbs distributions of the form . ILA augments…
Energy-based models for inverse imaging problems
Andreas Habring, Martin Holler, Thomas Pock +1
In this chapter we provide a thorough overview of the use of energy-based models (EBMs) in the context of inverse imaging problems. EBMs are probability distributions modeled via G…
Total Variation-Based Image Decomposition and Denoising for Microscopy Images
Marco Corrias, Giada Franceschi, Michele Riva +5
Experimentally acquired microscopy images are unavoidably affected by the presence of noise and other unwanted signals, which degrade their quality and might hide relevant features…
Diffusion at Absolute Zero: Langevin Sampling Using Successive Moreau Envelopes [conference paper]
Andreas Habring, Alexander Falk, Thomas Pock
In this article we propose a novel method for sampling from Gibbs distributions of the form with a potential . In particular, inspired by diffusion m…
Diffusion at Absolute Zero: Langevin Sampling using Successive Moreau Envelopes [journal paper]
Andreas Habring, Alexander Falk, Martin Zach +1
We propose a method for sampling from Gibbs distributions of the form by considering a family of approximations of the target density which is…
Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction
Lukas Glaszner, Martin Zach
Diffusion model have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for uncond…