Comparison of neural networks for suppression of multiplicative noise in images

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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Bibliographic Details
Published in:Kompʹûternaâ optika Vol. 48; no. 3; pp. 425 - 431
Main Authors: Pavlov, V.A., Belov, A.A., Nguen, V.T., Jovanovski, N., Ovsyannikova, A.S.
Format: Journal Article
Language:English
Russian
Published: 01-06-2024
Online Access:Get full text
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Summary: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.
ISSN:0134-2452
2412-6179
DOI:10.18287/2412-6179-CO-1400