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Significance Support Vector Regression for Image Denoising
As an extension of the support vector machine, support vector regression (SVR) plays a significant role in image denoising. However, due to ignoring the spatial distribution information of noisy pixels, the conventional SVR denoising model faces the bottleneck of overfitting in the case of serious n...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470107/ https://www.ncbi.nlm.nih.gov/pubmed/34573858 http://dx.doi.org/10.3390/e23091233 |
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author | Sun, Bing Liu, Xiaofeng |
author_facet | Sun, Bing Liu, Xiaofeng |
author_sort | Sun, Bing |
collection | PubMed |
description | As an extension of the support vector machine, support vector regression (SVR) plays a significant role in image denoising. However, due to ignoring the spatial distribution information of noisy pixels, the conventional SVR denoising model faces the bottleneck of overfitting in the case of serious noise interference, which leads to a degradation of the denoising effect. For this problem, this paper proposes a significance measurement framework for evaluating the sample significance with sample spatial density information. Based on the analysis of the penalty factor in SVR, significance SVR (SSVR) is presented by assigning the sample significance factor to each sample. The refined penalty factor enables SSVR to be less susceptible to outliers in the solution process. This overcomes the drawback that the SVR imposes the same penalty factor for all samples, which leads to the objective function paying too much attention to outliers, resulting in poorer regression results. As an example of the proposed framework applied in image denoising, a cutoff distance-based significance factor is instantiated to estimate the samples’ importance in SSVR. Experiments conducted on three image datasets showed that SSVR demonstrates excellent performance compared to the best-in-class image denoising techniques in terms of a commonly used denoising evaluation index and observed visual. |
format | Online Article Text |
id | pubmed-8470107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84701072021-09-27 Significance Support Vector Regression for Image Denoising Sun, Bing Liu, Xiaofeng Entropy (Basel) Article As an extension of the support vector machine, support vector regression (SVR) plays a significant role in image denoising. However, due to ignoring the spatial distribution information of noisy pixels, the conventional SVR denoising model faces the bottleneck of overfitting in the case of serious noise interference, which leads to a degradation of the denoising effect. For this problem, this paper proposes a significance measurement framework for evaluating the sample significance with sample spatial density information. Based on the analysis of the penalty factor in SVR, significance SVR (SSVR) is presented by assigning the sample significance factor to each sample. The refined penalty factor enables SSVR to be less susceptible to outliers in the solution process. This overcomes the drawback that the SVR imposes the same penalty factor for all samples, which leads to the objective function paying too much attention to outliers, resulting in poorer regression results. As an example of the proposed framework applied in image denoising, a cutoff distance-based significance factor is instantiated to estimate the samples’ importance in SSVR. Experiments conducted on three image datasets showed that SSVR demonstrates excellent performance compared to the best-in-class image denoising techniques in terms of a commonly used denoising evaluation index and observed visual. MDPI 2021-09-20 /pmc/articles/PMC8470107/ /pubmed/34573858 http://dx.doi.org/10.3390/e23091233 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sun, Bing Liu, Xiaofeng Significance Support Vector Regression for Image Denoising |
title | Significance Support Vector Regression for Image Denoising |
title_full | Significance Support Vector Regression for Image Denoising |
title_fullStr | Significance Support Vector Regression for Image Denoising |
title_full_unstemmed | Significance Support Vector Regression for Image Denoising |
title_short | Significance Support Vector Regression for Image Denoising |
title_sort | significance support vector regression for image denoising |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470107/ https://www.ncbi.nlm.nih.gov/pubmed/34573858 http://dx.doi.org/10.3390/e23091233 |
work_keys_str_mv | AT sunbing significancesupportvectorregressionforimagedenoising AT liuxiaofeng significancesupportvectorregressionforimagedenoising |