Superresolution with compound Markov random fields via the variational EM algorithm

DOI10.1016/J.NEUNET.2008.12.005zbMath1337.62303OpenAlexW2023770789WikidataQ51618289 ScholiaQ51618289MaRDI QIDQ280419

Shin Ishii, Shin-ichi Maeda, Atsunori Kanemura

Publication date: 10 May 2016

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2008.12.005

zbMATH Keywords

image registrationBayesian marginalizationcompound Markov random fieldsedge preservationimage superresolutionvariational EM algorithms

Mathematics Subject Classification ID

Random fields; image analysis (62M40)

Related Items (5)

An edge preserving high-order PDE for multiframe image super-resolution ⋮ Bilevel optimal parameter learning for a high-order nonlocal multiframe super-resolution problem ⋮ Kullback-Leibler divergence based composite prior modeling for Bayesian super-resolution ⋮ A nonlinear fourth-order PDE for multi-frame image super-resolution enhancement ⋮ Multi-frame image super-resolution reconstruction based on spatial information weighted fields of experts

Uses Software

• PRMLT

Cites Work

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• Bayesian representation learning in the cortex regulated by acetylcholine
• Factorial hidden Markov models
• An introduction to variational methods for graphical models
• Simulated annealing in compound Gaussian random fields (image processing)
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Statistical-mechanical approach to image processing
• Log-determinant relaxation for approximate inference in discrete Markov random fields
• A Variational Approach for Bayesian Blind Image Deconvolution
• An MRF-based approach to generation of super-resolution images from blurred observations

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