Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise

Images taken with different sensors and transmitted through different channels can be noisy. In such conditions, the image most often suffers from random-valued impulse noise. Denoising an image is an important part of image preprocessing before recognition by a neural network. The accuracy of image...

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Egile Nagusiak:	Orazaev, A. R., Оразаев, А. Р., Lyakhov, P. A., Ляхов, П. А., Baboshina, V. A., Бабошина, В. А., Kalita, D. I., Калита, Д. И.
Formatua:	Статья
Hizkuntza:	English
Argitaratua:	2023
Gaiak:	Neural networks Distorted pixel detector Image recognition Impulse noise Noise removal
Sarrera elektronikoa:	https://dspace.ncfu.ru/handle/20.500.12258/23497
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id	ir-20.500.12258-23497
record_format	dspace
spelling	ir-20.500.12258-234972023-05-24T13:50:15Z Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise Orazaev, A. R. Оразаев, А. Р. Lyakhov, P. A. Ляхов, П. А. Baboshina, V. A. Бабошина, В. А. Kalita, D. I. Калита, Д. И. Neural networks Distorted pixel detector Image recognition Impulse noise Noise removal Images taken with different sensors and transmitted through different channels can be noisy. In such conditions, the image most often suffers from random-valued impulse noise. Denoising an image is an important part of image preprocessing before recognition by a neural network. The accuracy of image recognition by a neural network directly depends on the intensity of image noise. This paper presents a three-stage image cleaning and recognition system, which includes a developed detector of pulsed noisy pixels, a filter for cleaning found noisy pixels based on an adaptive median, and a neural network program for recognizing cleaned images. It was noted that at low noise intensities, cleaning is practically not required, but noise with an intensity of more than 10% can seriously damage the image and reduce recognition accuracy. As a training base for noise, cleaning, and recognition, the CIFAR10 digital image database was used, consisting of 60,000 images belonging to 10 classes. The results show that the proposed neural network recognition system for images affected by to random-valued impulse noise effectively finds and corrects damaged pixels. This helped to increase the accuracy of image recognition compared to existing methods for cleaning random-valued impulse noise. 2023-05-24T13:48:14Z 2023-05-24T13:48:14Z 2023 Статья Orazaev, A., Lyakhov, P., Baboshina, V., Kalita, D. Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise // Applied Sciences (Switzerland). - 2023. - 13(3), art. no. 1585. - DOI: 10.3390/ app13031585 http://hdl.handle.net/20.500.12258/23497 en Applied Sciences (Switzerland) application/pdf application/pdf
institution	СКФУ
collection	Репозиторий
language	English
topic	Neural networks Distorted pixel detector Image recognition Impulse noise Noise removal
spellingShingle	Neural networks Distorted pixel detector Image recognition Impulse noise Noise removal Orazaev, A. R. Оразаев, А. Р. Lyakhov, P. A. Ляхов, П. А. Baboshina, V. A. Бабошина, В. А. Kalita, D. I. Калита, Д. И. Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise
description	Images taken with different sensors and transmitted through different channels can be noisy. In such conditions, the image most often suffers from random-valued impulse noise. Denoising an image is an important part of image preprocessing before recognition by a neural network. The accuracy of image recognition by a neural network directly depends on the intensity of image noise. This paper presents a three-stage image cleaning and recognition system, which includes a developed detector of pulsed noisy pixels, a filter for cleaning found noisy pixels based on an adaptive median, and a neural network program for recognizing cleaned images. It was noted that at low noise intensities, cleaning is practically not required, but noise with an intensity of more than 10% can seriously damage the image and reduce recognition accuracy. As a training base for noise, cleaning, and recognition, the CIFAR10 digital image database was used, consisting of 60,000 images belonging to 10 classes. The results show that the proposed neural network recognition system for images affected by to random-valued impulse noise effectively finds and corrects damaged pixels. This helped to increase the accuracy of image recognition compared to existing methods for cleaning random-valued impulse noise.
format	Статья
author	Orazaev, A. R. Оразаев, А. Р. Lyakhov, P. A. Ляхов, П. А. Baboshina, V. A. Бабошина, В. А. Kalita, D. I. Калита, Д. И.
author_facet	Orazaev, A. R. Оразаев, А. Р. Lyakhov, P. A. Ляхов, П. А. Baboshina, V. A. Бабошина, В. А. Kalita, D. I. Калита, Д. И.
author_sort	Orazaev, A. R.
title	Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise
title_short	Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise
title_full	Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise
title_fullStr	Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise
title_full_unstemmed	Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise
title_sort	neural network system for recognizing images affected by random-valued impulse noise
publishDate	2023
url	https://dspace.ncfu.ru/handle/20.500.12258/23497
work_keys_str_mv	AT orazaevar neuralnetworksystemforrecognizingimagesaffectedbyrandomvaluedimpulsenoise AT orazaevar neuralnetworksystemforrecognizingimagesaffectedbyrandomvaluedimpulsenoise AT lyakhovpa neuralnetworksystemforrecognizingimagesaffectedbyrandomvaluedimpulsenoise AT lâhovpa neuralnetworksystemforrecognizingimagesaffectedbyrandomvaluedimpulsenoise AT baboshinava neuralnetworksystemforrecognizingimagesaffectedbyrandomvaluedimpulsenoise AT babošinava neuralnetworksystemforrecognizingimagesaffectedbyrandomvaluedimpulsenoise AT kalitadi neuralnetworksystemforrecognizingimagesaffectedbyrandomvaluedimpulsenoise AT kalitadi neuralnetworksystemforrecognizingimagesaffectedbyrandomvaluedimpulsenoise
_version_	1809808384667418624

Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise

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