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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Bibliographic Details
Published in:Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Vol. 818; pp. 68 - 75
Main Authors: Li, Huailiang, Tuo, Xianguo, Shi, Rui, Zhang, Jinzhao, Henderson, Mark Julian, Courtois, Jérémie, Yan, Minhao
Format: Journal Article
Language:English
Published: Elsevier B.V 11.05.2016
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0168-9002
1872-9576
DOI:10.1016/j.nima.2016.02.047