Transformed Gaussian Random Fields for Unsupervised Image Deconvolution
Author:
Affiliation:
1. IRIMAS, UR 7499, Université de Haute-Alsace, Mulhouse, France
Funder
ANR HORUS
THTTM
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/97/9686799/10016681.pdf?arnumber=10016681
Reference21 articles.
1. Exploring a new class of non-stationary spatial gaussian random fields with varying local anisotropy;fuglstad;Stat Sinica,2015
2. Unsupervised image segmentation with Gaussian Pairwise Markov Fields
3. Riemann manifold Langevin and Hamiltonian Monte Carlo methods
4. The No-U-turn sampler: Adaptively setting path lengths in hamiltonian Monte Carlo;hoffman;J Mach Learn Res,2014
5. Stochastic representation and dimension reduction for non-Gaussian random fields: review and reflection
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1. Bayesian image segmentation under varying blur with triplet Markov random field;Inverse Problems;2024-08-14
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