Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain

The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the...
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Autor*in:	Sangyoon Lee [verfasserIn] Min Seok Lee [verfasserIn] Moon Gi Kang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	low-dose X-ray non-subsampled contourlet transform (NSCT) Poisson–Gaussian noise noise analysis noise estimation

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 18(2018), 4, p 1019
Übergeordnetes Werk:	volume:18 ; year:2018 ; number:4, p 1019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s18041019

Katalog-ID:	DOAJ025505173

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520			\|a The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods.
650		4	\|a low-dose X-ray
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allfieldsSound	10.3390/s18041019 doi (DE-627)DOAJ025505173 (DE-599)DOAJa16dca9e89de4ede96b86c2443f31cb0 DE-627 ger DE-627 rakwb eng TP1-1185 Sangyoon Lee verfasserin aut Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods. low-dose X-ray non-subsampled contourlet transform (NSCT) Poisson–Gaussian noise noise analysis noise estimation Chemical technology Min Seok Lee verfasserin aut Moon Gi Kang verfasserin aut In Sensors MDPI AG, 2003 18(2018), 4, p 1019 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:18 year:2018 number:4, p 1019 https://doi.org/10.3390/s18041019 kostenfrei https://doaj.org/article/a16dca9e89de4ede96b86c2443f31cb0 kostenfrei http://www.mdpi.com/1424-8220/18/4/1019 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 4, p 1019
language	English
source	In Sensors 18(2018), 4, p 1019 volume:18 year:2018 number:4, p 1019
sourceStr	In Sensors 18(2018), 4, p 1019 volume:18 year:2018 number:4, p 1019
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	low-dose X-ray non-subsampled contourlet transform (NSCT) Poisson–Gaussian noise noise analysis noise estimation Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Sangyoon Lee @@aut@@ Min Seok Lee @@aut@@ Moon Gi Kang @@aut@@
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callnumber-first	T - Technology
author	Sangyoon Lee
spellingShingle	Sangyoon Lee misc TP1-1185 misc low-dose X-ray misc non-subsampled contourlet transform (NSCT) misc Poisson–Gaussian noise misc noise analysis misc noise estimation misc Chemical technology Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain
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topic_title	TP1-1185 Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain low-dose X-ray non-subsampled contourlet transform (NSCT) Poisson–Gaussian noise noise analysis noise estimation
topic	misc TP1-1185 misc low-dose X-ray misc non-subsampled contourlet transform (NSCT) misc Poisson–Gaussian noise misc noise analysis misc noise estimation misc Chemical technology
topic_unstemmed	misc TP1-1185 misc low-dose X-ray misc non-subsampled contourlet transform (NSCT) misc Poisson–Gaussian noise misc noise analysis misc noise estimation misc Chemical technology
topic_browse	misc TP1-1185 misc low-dose X-ray misc non-subsampled contourlet transform (NSCT) misc Poisson–Gaussian noise misc noise analysis misc noise estimation misc Chemical technology
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title	Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain
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title_full	Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain
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title_sort	poisson–gaussian noise analysis and estimation for low-dose x-ray images in the nsct domain
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title_auth	Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain
abstract	The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods.
abstractGer	The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods.
abstract_unstemmed	The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods.
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title_short	Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain
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