Determining Neighborhoods of Image Pixels Automatically for Adaptive Image Denoising Using Nonlinear Time Series Analysis

This paper presents a method determining neighborhoods of the image pixels automatically in adaptive denoising. The neighborhood is named stationary neighborhood (SN). In this method, the noisy image is considered as an observation of a nonlinear time series (NTS). Image denoising must recover the t...

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Main Authors:	Zhiwu Liao, Shaoxiang Hu, Wufan Chen
Format:	Article
Language:	English
Published:	Hindawi Limited 2010-01-01
Series:	Mathematical Problems in Engineering
Online Access:	http://dx.doi.org/10.1155/2010/914564

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record_format	Article
spelling	doaj-02c6fd31bede40048f04da7f2fb7c8642020-11-24T20:40:14ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472010-01-01201010.1155/2010/914564914564Determining Neighborhoods of Image Pixels Automatically for Adaptive Image Denoising Using Nonlinear Time Series AnalysisZhiwu Liao0Shaoxiang Hu1Wufan Chen2Key Laboratory of Land Resources Evaluation and Monitoring of Southwest, Sichuan Normal University, Ministry of Education, Chengdu 610068, Sichuan, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, ChinaInstitute of Medical Information and Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, ChinaThis paper presents a method determining neighborhoods of the image pixels automatically in adaptive denoising. The neighborhood is named stationary neighborhood (SN). In this method, the noisy image is considered as an observation of a nonlinear time series (NTS). Image denoising must recover the true state of the NTS from the observation. At first, the false neighbors (FNs) in a neighborhood for each pixel are removed according to the context. After moving the FNs, we obtain an SN, where the NTS is stationary and the real state can be estimated using the theory of stationary time series (STS). Since each SN of an image pixel consists of elements with similar context and nearby locations, the method proposed in this paper can not only adaptively find neighbors and determine size of the SN according to the characteristics of a pixel, but also be able to denoise while effectively preserving edges. Finally, in order to show the superiority of this algorithm, we compare this method with the existing universal denoising algorithms.http://dx.doi.org/10.1155/2010/914564
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Zhiwu Liao Shaoxiang Hu Wufan Chen
spellingShingle	Zhiwu Liao Shaoxiang Hu Wufan Chen Determining Neighborhoods of Image Pixels Automatically for Adaptive Image Denoising Using Nonlinear Time Series Analysis Mathematical Problems in Engineering
author_facet	Zhiwu Liao Shaoxiang Hu Wufan Chen
author_sort	Zhiwu Liao
title	Determining Neighborhoods of Image Pixels Automatically for Adaptive Image Denoising Using Nonlinear Time Series Analysis
title_short	Determining Neighborhoods of Image Pixels Automatically for Adaptive Image Denoising Using Nonlinear Time Series Analysis
title_full	Determining Neighborhoods of Image Pixels Automatically for Adaptive Image Denoising Using Nonlinear Time Series Analysis
title_fullStr	Determining Neighborhoods of Image Pixels Automatically for Adaptive Image Denoising Using Nonlinear Time Series Analysis
title_full_unstemmed	Determining Neighborhoods of Image Pixels Automatically for Adaptive Image Denoising Using Nonlinear Time Series Analysis
title_sort	determining neighborhoods of image pixels automatically for adaptive image denoising using nonlinear time series analysis
publisher	Hindawi Limited
series	Mathematical Problems in Engineering
issn	1024-123X 1563-5147
publishDate	2010-01-01
description	This paper presents a method determining neighborhoods of the image pixels automatically in adaptive denoising. The neighborhood is named stationary neighborhood (SN). In this method, the noisy image is considered as an observation of a nonlinear time series (NTS). Image denoising must recover the true state of the NTS from the observation. At first, the false neighbors (FNs) in a neighborhood for each pixel are removed according to the context. After moving the FNs, we obtain an SN, where the NTS is stationary and the real state can be estimated using the theory of stationary time series (STS). Since each SN of an image pixel consists of elements with similar context and nearby locations, the method proposed in this paper can not only adaptively find neighbors and determine size of the SN according to the characteristics of a pixel, but also be able to denoise while effectively preserving edges. Finally, in order to show the superiority of this algorithm, we compare this method with the existing universal denoising algorithms.
url	http://dx.doi.org/10.1155/2010/914564
work_keys_str_mv	AT zhiwuliao determiningneighborhoodsofimagepixelsautomaticallyforadaptiveimagedenoisingusingnonlineartimeseriesanalysis AT shaoxianghu determiningneighborhoodsofimagepixelsautomaticallyforadaptiveimagedenoisingusingnonlineartimeseriesanalysis AT wufanchen determiningneighborhoodsofimagepixelsautomaticallyforadaptiveimagedenoisingusingnonlineartimeseriesanalysis
_version_	1716827781514395648

Determining Neighborhoods of Image Pixels Automatically for Adaptive Image Denoising Using Nonlinear Time Series Analysis

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