Comparison of neural networks for suppression of multiplicative noise in images

The paper compares several neural network (NN) architectures for suppression of multiplicative noise. The images may contain sharp boundaries and large homogeneous areas. Convolutional and fully connected networks are investigated. It is shown that different architectures require significantly diffe...

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Main Authors: V.A. Pavlov, A.A. Belov, V.T. Nguen, N. Jovanovski, A.S. Ovsyannikova
Format: Article
Language:English
Published: Samara National Research University 2024-06-01
Series:Компьютерная оптика
Subjects:
Online Access:https://www.computeroptics.ru/eng/KO/Annot/KO48-3/480313e.html
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author V.A. Pavlov
A.A. Belov
V.T. Nguen
N. Jovanovski
A.S. Ovsyannikova
author_facet V.A. Pavlov
A.A. Belov
V.T. Nguen
N. Jovanovski
A.S. Ovsyannikova
author_sort V.A. Pavlov
collection DOAJ
description The paper compares several neural network (NN) architectures for suppression of multiplicative noise. The images may contain sharp boundaries and large homogeneous areas. Convolutional and fully connected networks are investigated. It is shown that different architectures require significantly different amount of training data to reach the same noise suppression quality. Examples of NN requiring lower amounts of training data are presented.
format Article
id doaj-art-8ddcd6bb50b1491b97a00ab9d2c1e56b
institution Kabale University
issn 0134-2452
2412-6179
language English
publishDate 2024-06-01
publisher Samara National Research University
record_format Article
series Компьютерная оптика
spelling doaj-art-8ddcd6bb50b1491b97a00ab9d2c1e56b2025-02-06T12:25:17ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792024-06-0148342543110.18287/2412-6179-CO-1400Comparison of neural networks for suppression of multiplicative noise in imagesV.A. Pavlov0A.A. Belov1V.T. Nguen2N. Jovanovski3 A.S. Ovsyannikova4Peter the Great St. Petersburg Polytechnic UniversityPeter the Great St. Petersburg Polytechnic UniversityPeter the Great St. Petersburg Polytechnic UniversityPeter the Great St. Petersburg Polytechnic UniversityPeter the Great St. Petersburg Polytechnic UniversityThe paper compares several neural network (NN) architectures for suppression of multiplicative noise. The images may contain sharp boundaries and large homogeneous areas. Convolutional and fully connected networks are investigated. It is shown that different architectures require significantly different amount of training data to reach the same noise suppression quality. Examples of NN requiring lower amounts of training data are presented.https://www.computeroptics.ru/eng/KO/Annot/KO48-3/480313e.htmlspeckle noiseradar imagesarnoise reductionimage processingneural network
spellingShingle V.A. Pavlov
A.A. Belov
V.T. Nguen
N. Jovanovski
A.S. Ovsyannikova
Comparison of neural networks for suppression of multiplicative noise in images
Компьютерная оптика
speckle noise
radar image
sar
noise reduction
image processing
neural network
title Comparison of neural networks for suppression of multiplicative noise in images
title_full Comparison of neural networks for suppression of multiplicative noise in images
title_fullStr Comparison of neural networks for suppression of multiplicative noise in images
title_full_unstemmed Comparison of neural networks for suppression of multiplicative noise in images
title_short Comparison of neural networks for suppression of multiplicative noise in images
title_sort comparison of neural networks for suppression of multiplicative noise in images
topic speckle noise
radar image
sar
noise reduction
image processing
neural network
url https://www.computeroptics.ru/eng/KO/Annot/KO48-3/480313e.html
work_keys_str_mv AT vapavlov comparisonofneuralnetworksforsuppressionofmultiplicativenoiseinimages
AT aabelov comparisonofneuralnetworksforsuppressionofmultiplicativenoiseinimages
AT vtnguen comparisonofneuralnetworksforsuppressionofmultiplicativenoiseinimages
AT njovanovski comparisonofneuralnetworksforsuppressionofmultiplicativenoiseinimages
AT asovsyannikova comparisonofneuralnetworksforsuppressionofmultiplicativenoiseinimages