A de-noising algorithm to improve SNR of segmented gamma scanner for spectrum analysis

An improved threshold shift-invariant wavelet transform de-noising algorithm for high-resolution gamma-ray spectroscopy is proposed to optimize the threshold function of wavelet transforms and reduce signal resulting from pseudo-Gibbs artificial fluctuations. This algorithm was applied to a segmente...

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Vydané v:	Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Ročník 818; s. 68 - 75
Hlavní autori:	Li, Huailiang, Tuo, Xianguo, Shi, Rui, Zhang, Jinzhao, Henderson, Mark Julian, Courtois, Jérémie, Yan, Minhao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 11.05.2016
Predmet:	Algorithms De-noising Detectors Quantitative analysis Scanning Shift-invariant wavelet transform Spectrum analysis Thresholds Wave attenuation Wavelet transforms γ-spectrum analysis DWT De-noising CWT AC FOM Shift-invariant wavelet transform RMSE S-I WT ABS γ-spectrum analysis SNR HRGS WT SGS
ISSN:	0168-9002, 1872-9576
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Abstract	An improved threshold shift-invariant wavelet transform de-noising algorithm for high-resolution gamma-ray spectroscopy is proposed to optimize the threshold function of wavelet transforms and reduce signal resulting from pseudo-Gibbs artificial fluctuations. This algorithm was applied to a segmented gamma scanning system with large samples in which high continuum levels caused by Compton scattering are routinely encountered. De-noising data from the gamma ray spectrum measured by segmented gamma scanning system with improved, shift-invariant and traditional wavelet transform algorithms were all evaluated. The improved wavelet transform method generated significantly enhanced performance of the figure of merit, the root mean square error, the peak area, and the sample attenuation correction in the segmented gamma scanning system assays. We also found that the gamma energy spectrum can be viewed as a low frequency signal as well as high frequency noise superposition by the spectrum analysis. Moreover, a smoothed spectrum can be appropriate for straightforward automated quantitative analysis.
AbstractList	An improved threshold shift-invariant wavelet transform de-noising algorithm for high-resolution gamma-ray spectroscopy is proposed to optimize the threshold function of wavelet transforms and reduce signal resulting from pseudo-Gibbs artificial fluctuations. This algorithm was applied to a segmented gamma scanning system with large samples in which high continuum levels caused by Compton scattering are routinely encountered. De-noising data from the gamma ray spectrum measured by segmented gamma scanning system with improved, shift-invariant and traditional wavelet transform algorithms were all evaluated. The improved wavelet transform method generated significantly enhanced performance of the figure of merit, the root mean square error, the peak area, and the sample attenuation correction in the segmented gamma scanning system assays. We also found that the gamma energy spectrum can be viewed as a low frequency signal as well as high frequency noise superposition by the spectrum analysis. Moreover, a smoothed spectrum can be appropriate for straightforward automated quantitative analysis.
Author	Courtois, Jérémie Li, Huailiang Zhang, Jinzhao Henderson, Mark Julian Yan, Minhao Shi, Rui Tuo, Xianguo
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SubjectTerms	Algorithms De-noising Detectors Quantitative analysis Scanning Shift-invariant wavelet transform Spectrum analysis Thresholds Wave attenuation Wavelet transforms γ-spectrum analysis
Title	A de-noising algorithm to improve SNR of segmented gamma scanner for spectrum analysis
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