Micro-spectrometer signal denoising method based on stable wavelet transform

A micro-spectrometer and stationary wavelet technology, which is applied in the field of signal processing and instrumentation, can solve the problem of noise in the spectral signal of the micro-spectrometer, and achieve the effects of ensuring authenticity, suppressing Gibbs oscillation, and high signal-to-noise ratio

Inactive Publication Date: 2012-12-12
ZHEJIANG UNIV
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is to overcome the deficiencies in the prior art, provide a method for denoising the sig

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  • Micro-spectrometer signal denoising method based on stable wavelet transform
  • Micro-spectrometer signal denoising method based on stable wavelet transform
  • Micro-spectrometer signal denoising method based on stable wavelet transform

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Embodiment Construction

[0041] The realization process of the present invention can be briefly summarized as:

[0042] 1. Selecting an appropriate wavelet base, the Symlet5 wavelet base is adopted in the present invention.

[0043] 2. Select the layer number N for stationary wavelet decomposition, N=5 in the present invention.

[0044] 3. Use the selected wavelet basis and decomposition layers to perform stationary wavelet transform on the noisy spectral signal, and obtain the approximation coefficient and detail coefficient of the spectral signal stationary wavelet transform.

[0045] 4. Use the soft threshold function and Heuristic SURE threshold selection rules to determine the threshold of stationary wavelet denoising.

[0046] 5. Use the threshold processing method determined in step 4 to process the spectral signal detail stationary wavelet coefficients obtained in step 3, and save the new detail coefficients.

[0047] 6. Use the thresholded new detail coefficients obtained in step 5 and the ...

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Abstract

The invention relates to signal processing and aims at providing a micro-spectrometer signal denoising method based on stable wavelet transform. The method includes subjecting a spectrum signal generated by a micro-spectrometer to stable wavelet decomposition to obtain a detail coefficient and an approximation coefficient of a stable wavelet of the spectrum signal; using methods of a soft threshold function and a multi-resolution heuristic threshold selection rule to transform the detail coefficient so as to remove noises and saving the detail coefficient as a new stable wavelet detail coefficient; and subjecting the new detail coefficient and the prior approximation coefficient to stable wavelet inverse transformation to obtain a denoised micro-spectrometer spectrum signal. The micro-spectrometer signal denoising method is invariant in translation and capable of effectively inhibiting a Gibbs oscillation phenomenon and guaranteeing facticity of signals, fake peaks can not occur to line spectrum signals, and geometrical characteristics of the denoised spectrum signal are identical to those of original signals; and compared with other threshold selection methods, the micro-spectrometer signal denoising method is capable of obtaining a higher signal to noise ratio and better denoising continuous spectrum signals and the line spectrum signals.

Description

technical field [0001] The invention belongs to the field of signal processing and instruments, in particular to the field of micro-spectrometer spectral signal processing, in particular to a spectral signal threshold processing method based on stationary wavelet transform. Background technique [0002] With the development of microelectronics technology and microprocessing technology, micro spectrometers are used more and more widely, including aviation, agriculture, food, medicine, chemistry and other fields. However, the actual measured spectral signal is often accompanied by noise interference. The noise of the spectral signal includes the random noise of the CCD itself, the noise caused by uneven pixel sensitivity, and the quantization noise caused by A / D conversion. The noise type is white noise. . Therefore, it is necessary to denoise the measured spectral signal to minimize the influence of noise on the useful spectral signal. [0003] Mean filtering is the simples...

Claims

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Application Information

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IPC IPC(8): G01J3/28
Inventor 刘岩余飞鸿
Owner ZHEJIANG UNIV
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