Kullback-Leibler divergence based composite prior modeling for Bayesian super-resolution

DOI10.1007/s10915-013-9784-yzbMath1356.94025OpenAlexW2024252887MaRDI QIDQ474924

Hai-Song Deng, Wen-Ze Shao, Zhi-Hui Wei

Publication date: 25 November 2014

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10915-013-9784-y

zbMATH Keywords

total variationKullback-Leibler divergencesuper-resolutionFrobenius norm regularizationposterior mean estimation

Mathematics Subject Classification ID

Bayesian inference (62F15) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (1)

A framelet sparse reconstruction method for pansharpening with guaranteed convergence

Uses Software

• RecPF

Cites Work

• Superresolution with compound Markov random fields via the variational EM algorithm
• Image super-resolution by TV-regularization and Bregman iteration
• A total variation regularization based super-resolution reconstruction algorithm for digital video
• Adaptive total variation image deblurring: a majorization-minimization approach
• Schubert functors and Schubert polynomials
• Iterative image restoration combining total variation minimization and a second-order functional
• Fast Image Recovery Using Variable Splitting and Constrained Optimization
• Image Super-Resolution Via Sparse Representation
• Variational Bayesian Super Resolution
• Hessian-Based Norm Regularization for Image Restoration With Biomedical Applications

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