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A novel image noise reduction method for composite multistable stochastic resonance systems
In the field of digital signal processing, image denoising is an more and more significant research direction. For the traditional noise reduction theory, noise is considered to be harmful, and the image quality can be improved by analyzing noise characteristics and filtering noise. The appearance o...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025155/ https://www.ncbi.nlm.nih.gov/pubmed/36950586 http://dx.doi.org/10.1016/j.heliyon.2023.e14431 |
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author | Jiao, Shangbin Shi, Jiaqiang Wang, Yi Wang, Ruijie |
author_facet | Jiao, Shangbin Shi, Jiaqiang Wang, Yi Wang, Ruijie |
author_sort | Jiao, Shangbin |
collection | PubMed |
description | In the field of digital signal processing, image denoising is an more and more significant research direction. For the traditional noise reduction theory, noise is considered to be harmful, and the image quality can be improved by analyzing noise characteristics and filtering noise. The appearance of stochastic resonance theory proves that noise can be used to enhance signal, which brings new inspiration to image processing. The classical bistable stochastic resonance model has the problems of high potential barrier and easy saturation, which is not conducive to the improvement of image denoising effect. In this paper, a novel type of stochastic resonance potential well model is quoted, which solves the above shortcomings of the bistable stochastic resonance model, and then combines it with the Gaussian model to propose a composite multistable stochastic resonance model. The dynamic principle of the model in signal detection is described, and the influence of system parameters on image noise reduction is analyzed. The whale optimization algorithm is used to optimize the model parameters, and an adaptive compound multistable stochastic resonance system is established to process pictures and measured radar images under different noise backgrounds. The simulation experiment and engineering application show that the model proposed in this paper solves the problem of high potential barrier and easy saturation of the bistable model, and has better image noise reduction ability compared with Wiener filter, median filter, classical bistable stochastic resonance system and novel type of stochastic resonance potential well system. |
format | Online Article Text |
id | pubmed-10025155 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-100251552023-03-21 A novel image noise reduction method for composite multistable stochastic resonance systems Jiao, Shangbin Shi, Jiaqiang Wang, Yi Wang, Ruijie Heliyon Research Article In the field of digital signal processing, image denoising is an more and more significant research direction. For the traditional noise reduction theory, noise is considered to be harmful, and the image quality can be improved by analyzing noise characteristics and filtering noise. The appearance of stochastic resonance theory proves that noise can be used to enhance signal, which brings new inspiration to image processing. The classical bistable stochastic resonance model has the problems of high potential barrier and easy saturation, which is not conducive to the improvement of image denoising effect. In this paper, a novel type of stochastic resonance potential well model is quoted, which solves the above shortcomings of the bistable stochastic resonance model, and then combines it with the Gaussian model to propose a composite multistable stochastic resonance model. The dynamic principle of the model in signal detection is described, and the influence of system parameters on image noise reduction is analyzed. The whale optimization algorithm is used to optimize the model parameters, and an adaptive compound multistable stochastic resonance system is established to process pictures and measured radar images under different noise backgrounds. The simulation experiment and engineering application show that the model proposed in this paper solves the problem of high potential barrier and easy saturation of the bistable model, and has better image noise reduction ability compared with Wiener filter, median filter, classical bistable stochastic resonance system and novel type of stochastic resonance potential well system. Elsevier 2023-03-13 /pmc/articles/PMC10025155/ /pubmed/36950586 http://dx.doi.org/10.1016/j.heliyon.2023.e14431 Text en © 2023 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Jiao, Shangbin Shi, Jiaqiang Wang, Yi Wang, Ruijie A novel image noise reduction method for composite multistable stochastic resonance systems |
title | A novel image noise reduction method for composite multistable stochastic resonance systems |
title_full | A novel image noise reduction method for composite multistable stochastic resonance systems |
title_fullStr | A novel image noise reduction method for composite multistable stochastic resonance systems |
title_full_unstemmed | A novel image noise reduction method for composite multistable stochastic resonance systems |
title_short | A novel image noise reduction method for composite multistable stochastic resonance systems |
title_sort | novel image noise reduction method for composite multistable stochastic resonance systems |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025155/ https://www.ncbi.nlm.nih.gov/pubmed/36950586 http://dx.doi.org/10.1016/j.heliyon.2023.e14431 |
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