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

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

Bibekananda Jena, Punyaban Patel, G. Sinha

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引用次数: 29

摘要

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

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

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)

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Int. J. Rough Sets Data Anal.

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