An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

Int. J. Rough Sets Data Anal. Pub Date : 2018-04-01 DOI:10.4018/IJRSDA.2018040108

Bibekananda Jena, Punyaban Patel, G. Sinha

{"title":"An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter","authors":"Bibekananda Jena, Punyaban Patel, G. Sinha","doi":"10.4018/IJRSDA.2018040108","DOIUrl":null,"url":null,"abstract":"AnewtechniqueforsuppressionofRandomvaluedimpulsenoisefromthecontaminateddigital imageusingBackPropagationNeuralNetworkisproposedinthispaper.Thealgorithmsconsistof twostagesi.e.DetectionofImpulsenoiseandFilteringofidentifiednoisypixels.Toclassifybetween noisyandnon-noisyelementpresentintheimageafeed-forwardneuralnetworkhasbeentrained withwell-knownbackpropagationalgorithminthefirststage.Tomakethedetectionmethodmore accurate,Emphasishasbeengivenonselectionofproperinputandgenerationoftrainingpatterns. Thecorruptedpixelsareundergoingnon-localmeanfilteringemployedinthesecondstage.The effectivenessoftheproposedtechniqueisevaluatedusingwellknownstandarddigitalimagesat different levelof impulsenoise.Experiments show that themethodproposedherehasexcellent impulsenoisesuppressioncapability. KEywoRDS Artificial Neural Network (ANN), Image Denoising, Peak Signal-to-Noise Ratio (PSNR), Random Valued Impulse Noise (RVIN)","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"117 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Rough Sets Data Anal.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJRSDA.2018040108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 29

Abstract

AnewtechniqueforsuppressionofRandomvaluedimpulsenoisefromthecontaminateddigital imageusingBackPropagationNeuralNetworkisproposedinthispaper.Thealgorithmsconsistof twostagesi.e.DetectionofImpulsenoiseandFilteringofidentifiednoisypixels.Toclassifybetween noisyandnon-noisyelementpresentintheimageafeed-forwardneuralnetworkhasbeentrained withwell-knownbackpropagationalgorithminthefirststage.Tomakethedetectionmethodmore accurate,Emphasishasbeengivenonselectionofproperinputandgenerationoftrainingpatterns. Thecorruptedpixelsareundergoingnon-localmeanfilteringemployedinthesecondstage.The effectivenessoftheproposedtechniqueisevaluatedusingwellknownstandarddigitalimagesat different levelof impulsenoise.Experiments show that themethodproposedherehasexcellent impulsenoisesuppressioncapability. KEywoRDS Artificial Neural Network (ANN), Image Denoising, Peak Signal-to-Noise Ratio (PSNR), Random Valued Impulse Noise (RVIN)

查看原文本刊更多论文

基于人工神经网络和非局部均值滤波的随机值脉冲噪声抑制技术

AnewtechniqueforsuppressionofRandomvaluedimpulsenoisefromthecontaminateddigital imageusingBackPropagationNeuralNetworkisproposedinthispaper。Thealgorithmsconsistof twostagesi.e.DetectionofImpulsenoiseandFilteringofidentifiednoisypixels。Toclassifybetween noisyandnon-noisyelementpresentintheimageafeed-forwardneuralnetworkhasbeentrained withwell-knownbackpropagationalgorithminthefirststage。Tomakethedetectionmethodmore准确，Emphasishasbeengivenonselectionofproperinputandgenerationoftrainingpatterns。Thecorruptedpixelsareundergoingnon-localmeanfilteringemployedinthesecondstage。The effectivenessoftheproposedtechniqueisevaluatedusingwellknownstandarddigitalimagesat different[不同]levelof impulsenoise。Experiments show_ that_ themethodproposedherehasexcellent impulsenoisesuppressioncapability。关键词:人工神经网络，图像去噪，峰值信噪比，随机值脉冲噪声

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Int. J. Rough Sets Data Anal.

自引率

0.00%

发文量