Reducing Gaussian noise using distributed approximating functionals

DOI10.1016/S0010-4655(02)00457-5zbMath1017.65001WikidataQ59657200 ScholiaQ59657200MaRDI QIDQ1862423

Donald J. Kouri, David K. Hoffman, Eli Pollak

Publication date: 19 March 2003

Published in: Computer Physics Communications (Search for Journal in Brave)

zbMATH Keywords

performanceGaussian noisenoise reductiondigital image restorationdenoising characteristicsHermite distributed approximation functionalsMonte Carlo method filteringrepresentation of experimental data

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Monte Carlo methods (65C05) Numerical optimization and variational techniques (65K10) Signal detection and filtering (aspects of stochastic processes) (60G35) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesian Methods in Nonlinear Digital Image Restoration
• Hierarchy of Local Minimum Solutions of Heisenberg's Uncertainty Principle

This page was built for publication: Reducing Gaussian noise using distributed approximating functionals