Please use this identifier to cite or link to this item: http://elar.urfu.ru/handle/10995/102048
Title: Restoration of Static JPEG Images and RGB Video Frames by Means of Nonlinear Filtering in Conditions of Gaussian and Non-Gaussian Noise
Authors: Sokolov, R. I.
Abdullin, R. R.
Issue Date: 2017
Publisher: Blackwell Publishing Ltd
Citation: Sokolov R. I. Restoration of Static JPEG Images and RGB Video Frames by Means of Nonlinear Filtering in Conditions of Gaussian and Non-Gaussian Noise / R. I. Sokolov, R. R. Abdullin. — DOI 10.1002/2017RS006392 // Radio Science. — 2017. — Vol. 52. — Iss. 11. — P. 1363-1373.
Abstract: The use of nonlinear Markov process filtering makes it possible to restore both video stream frames and static photos at the stage of preprocessing. The present paper reflects the results of research in comparison of these types image filtering quality by means of special algorithm when Gaussian or non-Gaussian noises acting. Examples of filter operation at different values of signal-to-noise ratio are presented. A comparative analysis has been performed, and the best filtered kind of noise has been defined. It has been shown the quality of developed algorithm is much better than quality of adaptive one for RGB signal filtering at the same a priori information about the signal. Also, an advantage over median filter takes a place when both fluctuation and pulse noise filtering. ©2017. American Geophysical Union. All Rights Reserved.
Keywords: IMAGE RESTORATION
NON-GAUSSIAN NOISE
NONLINEAR MARKOV FILTERING
SIGNAL PROCESSING
GAUSSIAN DISTRIBUTION
IMAGE PROCESSING
IMAGE RECONSTRUCTION
INFORMATION FILTERING
MARKOV PROCESSES
MEDIAN FILTERS
NONLINEAR FILTERING
RESTORATION
SIGNAL PROCESSING
SIGNAL TO NOISE RATIO
COMPARATIVE ANALYSIS
FILTER OPERATIONS
IMAGE FILTERING
NON-GAUSSIAN NOISE
PRIORI INFORMATION
PROCESS FILTERING
SIGNAL FILTERING
SPECIAL ALGORITHMS
GAUSSIAN NOISE (ELECTRONIC)
URI: http://elar.urfu.ru/handle/10995/102048
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85038216671
PURE ID: 6212436
953138a2-bf81-4b01-8e65-ff3d7b42ae40
ISSN: 486604
DOI: 10.1002/2017RS006392
Appears in Collections:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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