De-noising of spectral signal based on stationary wavelet transform

Xu, Zhe; Zhang, Di

doi:10.1109/iceice.2011.5777848

Search citation statements

Order By: Relevance

Paper Sections

Select...

B Threshold Selection Rules1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2012

Publication Types

Select...

Other2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reference [8] mentioned the use of default thresholding rule, sqtwolog rule, and force thresholding rule in the spectral denoise, but all the three methods are rough rule to spectral denoise and have high denoise risk, therefore, they can not meet the accuracy requirements. So we must refine the selection of threshold, multiscale SURE, minimax and penalize algorithm will discussed in the following part.…”

Section: B Threshold Selection Rulesmentioning

confidence: 98%

Denoising of Spectral Signal in Miniature Spectrometer Based on Stationary Wavelet Transform

Liu

2012

2012 Symposium on Photonics and Optoelectronics

View full text Add to dashboard Cite

In process of real time acquisition and transmission, the spectral signal obtained by miniature spectrometer may contain noise signals superposed on it. But the traditional denoising methods can't achieve the expected performance because of their limitations. A de-noising method for spectral signal in miniature spectrometer based on Stationary Wavelet Transform (SWT) is proposed. We compared the performance of our de-nosing method with that of the traditional method. Simulation and experimental results prove that using sym5 basis, decomposition at level 5, soft shrinkage function with heuristic SURE threshold rule achieved superior performance in miniature spectrometer spectral signal denoising.

show abstract

Section: B Threshold Selection Rulesmentioning

confidence: 98%