Bayesian image superresolution and hidden variable modeling

DOI10.1007/S11424-010-9277-0zbMath1298.94012OpenAlexW2035632544MaRDI QIDQ469640

Wataru Fukuda, Atsunori Kanemura, Shin Ishii, Shin-ichi Maeda

Publication date: 11 November 2014

Published in: Journal of Systems Science and Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11424-010-9277-0

zbMATH Keywords

Bayesian estimationMarkov random fieldsimage superresolutionhidden variablesvariational estimation

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Uses Software

• PRMLT

Cites Work

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• Overcoming registration uncertainty in image super-resolution: maximize or marginalize?
• 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
• A Simplex Method for Function Minimization

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