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Wavelet median near-infrared spectrum denoising method and device

A near-infrared spectrum and wavelet threshold denoising technology, which is applied in the field of near-infrared spectrum signal preprocessing, can solve the problem that pulse noise cannot be filtered well, and achieve the effects of filtering pulse noise, improving signal-to-noise ratio, and improving robustness

Active Publication Date: 2016-08-31
CHONGQING INST OF GREEN & INTELLIGENT TECH CHINESE ACADEMY OF SCI
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Problems solved by technology

[0004] In order to overcome the deficiency that the pulse noise cannot be filtered well in wavelet threshold denoising, the present invention proposes a wavelet median near-infrared spectrum denoising method and device

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  • Wavelet median near-infrared spectrum denoising method and device

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Embodiment Construction

[0022] The present invention uses the upper and lower thresholds to select the wavelet coefficients of easily distinguishable signals and noises, and then uses correlation denoising for the difficultly distinguishable wavelet coefficients; calculates the variance of the extreme point of the spectrum after wavelet denoising, and the variance is greater than that of the left and right neighbors. The domain variance is the impulse noise for median filtering, and the value smaller than the neighborhood variance is the useful signal. After the above processing, the signal-to-noise ratio of the spectrum can be improved, and the robustness of the model can be improved.

[0023] As a first aspect of the invention, please refer to figure 1 , provides a wavelet median near-infrared spectrum denoising method, including:

[0024] Step 1, performing wavelet transform on the spectral signal, so as to obtain the wavelet coefficients of each layer;

[0025] Step 2, performing wavelet thresh...

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Abstract

The invention relates to a wavelet median near-infrared spectrum denoising method and device. The method includes: step 1, performing wavelet transformation on the spectral signal, thereby obtaining the wavelet coefficients of each layer; step 2, performing wavelet threshold denoising on the near-infrared spectrum according to the set upper threshold and lower threshold; step 3, denoising the The noised wavelet coefficients are reconstructed according to the inverse wavelet transform; step 4, the extreme value median filtering is performed on the reconstructed spectrum to obtain the denoised spectrum. The present invention combines the interlayer correlation of wavelet transform, which can better retain the wavelet coefficients of signal points; the improved extreme value median filtering can effectively retain the spectral signals of extreme value points, and filter out the pulse noise of extreme value points . After the above processing, the signal-to-noise ratio of the spectrum can be improved, and the robustness of the model can be improved.

Description

technical field [0001] The invention relates to a near-infrared spectrum signal preprocessing technology, in particular to a wavelet median near-infrared spectrum denoising method and device. Background technique [0002] Near-infrared spectroscopy is a rapidly developing high-tech analysis technology in the field of analytical chemistry. It has the advantages of non-destructive, non-polluting, good analysis reproducibility, and low cost. It is widely used in petrochemical, agricultural, food and other industries. Near-infrared spectroscopy refers to the near-infrared absorption spectrum in the wavelength range of 780nm-2500nm. Near-infrared spectroscopy can effectively reflect the characteristic information of hydrogen-containing groups in organic molecules in samples. Therefore, near-infrared spectroscopy can be used to quantitatively analyze hydrogen-containing atomic groups such as C-H, O-H, and N-H in compounds. [0003] However, there are certain noises in the origina...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N21/359
Inventor 赵健刘永福何国田徐泽宇林远长朱晓强何骥鸣吴姣娇何瑞英石珍
Owner CHONGQING INST OF GREEN & INTELLIGENT TECH CHINESE ACADEMY OF SCI
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