CN106644075A - Efficient de-noising method for Fourier spectrograph - Google Patents
Efficient de-noising method for Fourier spectrograph Download PDFInfo
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- G—PHYSICS
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- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
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- G01J3/28—Investigating the spectrum
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- G—PHYSICS
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- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
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Abstract
The invention relates to the technical field of spectral analysis and more particularly to an efficient de-noising method for a Fourier spectrograph. Compared with the prior art, the present invention provides a Fourier spectroscopic data linear filtering method based on frequency domain analysis. For a sine wave with a single frequency, the peak-to-peak contrast ratio of a target signal subjected to frequency domain filtering is 19.96 which is 12.5% higher than 17.74 that is the peak-to-peak value without the frequency domain filtering, which indicates that the method has better signal restoration performance than a traditional method under the influence of nonlinear noise.
Description
Technical field
The invention belongs to field of spectral analysis technology, and in particular to a kind of efficient denoising method of fourier spectrometer, its
It is the data processing method filtered based on frequency-domain analysis for Fourier spectrometer, before spectrogram is rebuild.
Background technology
Fourier spectrometer obtains spectrogram by carrying out Fourier transformation to interference pattern, with multichannel, high flux, height
Spectral resolution, the advantage for measuring the uniqueness such as quick and high s/n ratio.The data processing method of standard, bag have been defined at present
Include data prediction, apodization, phasing, zero padding and Fourier transformation.However, frequency domain filtering as a kind of conventional noise at
Reason method do not have not wherein, its reason be after spectrogram is obtained can according to the characteristic of hardware, the such as wave-length coverage of light source,
Directly determining the validity scope of spectrogram, such way is actually built upon Fourier to explorer response scope etc.
All processing methods before conversion are on linear hypothesis basis, but often true really not so, zero padding and phase
The method that nonlinear transformation can be introduced during bit correction, therefore before the filtering on spectrogram and Fourier transformation
Filtering and inequivalence, therefore in order to remove the noise in the range of non-targeted spectral coverage, needed before Fourier transformation, especially right and wrong
Carry out bandpass filtering before linear operation to reduce impact of the noise for data accuracy.
The content of the invention
(1) technical problem to be solved
The technical problem to be solved in the present invention is：It is linear how a kind of Fourier spectrum data based on frequency-domain analysis are provided
Filter processing method, is solved due to the spectroscopic data Problem-Error caused by Nonlinear Processing with hoping.
(2) technical scheme
To solve above-mentioned technical problem, the present invention provides a kind of efficient denoising method of fourier spectrometer, the method bag
Include following steps：
Step 1：Data prediction；
The data of experiment are read from the detector of fourier spectrometer, and the data of acquisition pass through first LPF
Method remove DC component, data afterwards eliminate trend term according to the mode of linear fit, then by the result root for being obtained
Suppress random noise disturbance according to the statistical distribution modeling of noise；
Step 2：Frequency domain filtering；
The data that step 1 is obtained, according to the target spectral coverage of instrument, by discrete cosine transform optical path difference-interference are generated
Wave filter in intensity, the data obtained to step 1 with the wave filter are filtered operation；
Step 3：Apodization；
The data obtained by step 2, are multiplied by selecting suitable apodizing function, the data to being obtained, root
The parameter in algorithmic procedure is determined according to the method for data statistics, the discontinuity at edge is relaxed by adding window, make the height of secondary lobe
Degree levels off to zero, so that energy is relatively concentrated in main lobe, obtains being closer to real frequency spectrum；
Step 4：Phasing；
Used as input, the purpose of phasing constitutes first the Hanning window of a N point to data using step 3 acquisition；
Then Hanning window seeks convolution to oneself, obtains the convolution window of 2N-1 points, and the sum of the convolution window of 2N-1 points is sought afterwards, will
Each item of convolution window obtains the normalization convolution window of 2N-1 points divided by the sum of convolution window, by the 1 of the data obtained：2N-1 items and
Normalization convolution window is multiplied, and obtains the 2N-1 items of adding window, afterwards by the 1st and N+1 items, the 2nd and N+2 item ... N-1 items and
2N-1 items are added, and obtain the N point sequences through complete mutually pretreatment；
Step 5：Zero padding；
On the basis of step 4 obtains data, Fourier transformation requires that it is 2 to calculate dataNIt is individual, and gather the dry of acquisition
Relate to diagram data to tend not to enough meet this condition, the interference pattern data padding that step 4 is obtained to 2NIt is individual；
Step 6：Fourier transformation；
The interference pattern processed through step 5 is converted to by spectrogram by Fourier transformation.
Wherein, apodizing function includes rectangle apodizing function.
Wherein, window function includes quarter window, trapezoid window, Hanning windows, Blackman window, Gaussian function, Norton-
Beer functions.
(3) beneficial effect
Compared with prior art, the present invention is provided at a kind of Fourier spectrum data linear filtering based on frequency-domain analysis
Reason method, for the dextrorotation ripple of single-frequency, the peak-to-peak contrast for adopting target model after frequency domain filtering is not adopted for 19.96 ratios
The peak-to-peak value 17.74 of frequency domain filtering has been higher by 12.5%, illustrates this method going back for signal under the influence of nonlinear noise
Proper energy power has preferably performance than conventional method.
Description of the drawings
Fig. 1 is the method flow diagram of technical solution of the present invention.
Fig. 2 is the filter schematic in target light spectral domain.
Fig. 3 is the filter schematic in the optical path difference-intensity domain after Fourier transformation.
Fig. 4 is the sinusoidal signal schematic diagram for introducing nonlinear noise.
Fig. 5 is using the transformation results schematic diagram of frequency domain filtering method.
Fig. 6 is the transformation results schematic diagram for not adopting frequency domain filtering method.
Specific embodiment
To make the purpose of the present invention, content and advantage clearer, with reference to the accompanying drawings and examples, to the present invention's
Specific embodiment is described in further detail.
To solve above-mentioned technical problem, the present invention provides a kind of efficient denoising method of fourier spectrometer, such as Fig. 1 institutes
Show, the method comprises the steps：
Step 1：Data prediction；
The data of experiment are read from the detector of fourier spectrometer, and the data of acquisition pass through first LPF
Method remove DC component, data afterwards eliminate trend term according to the mode of linear fit, then by the result root for being obtained
Suppress random noise disturbance according to the statistical distribution modeling of noise；
Wherein, remove DC component to be determined according to actual conditions, remove DC component in circuit design part sometimes,
Therefore according to specific needs being selected；
Wherein, the trend term is：Due to reasons such as measuring system performance, veiling glare and operations, a line can be produced
Property or gradual change trend error；
Step 2：Frequency domain filtering；
The data that step 1 is obtained, according to the target spectral coverage of instrument, by discrete cosine transform optical path difference-interference are generated
Wave filter in intensity, the data obtained to step 1 with the wave filter are filtered operation, and the order of the frequency domain filtering exists
After data prediction, before other all of data processing steps；
Step 3：Apodization；
The data obtained by step 2, are multiplied by selecting suitable apodizing function, the data to being obtained, root
The parameter in algorithmic procedure is determined according to the method for data statistics, the discontinuity at edge is relaxed by adding window, make the height of secondary lobe
Degree levels off to zero, so that energy is relatively concentrated in main lobe, obtains being closer to real frequency spectrum；
Discrete spectrum analysis is finite length due to time domain truncation, unavoidably there is energy leakage phenomenon, derivative spectomstry
Distortion, produces secondary lobe phenomenon, affects the accurate measurement of neighbouring spectral line especially weaker spectral line；Thus, using apodization, by adding
Relaxing the discontinuity at edge, the height for making secondary lobe levels off to zero to window, so that energy is relatively concentrated in main lobe, obtains more
Jie Jin real frequency spectrum；
Wherein, apodizing function includes rectangle apodizing function；
Wherein, window function includes quarter window, trapezoid window, Hanning windows, Blackman window, Gaussian function, Norton-
Beer functions；
Step 4：Phasing；
Used as input, the purpose of phasing constitutes first the Hanning window of a N point to data using step 3 acquisition；
Then Hanning window seeks convolution to oneself, obtains the convolution window of 2N-1 points, and the sum of the convolution window of 2N-1 points is sought afterwards, will
Each item of convolution window obtains the normalization convolution window of 2N-1 points divided by the sum of convolution window, by the 1 of the data obtained：2N-1 items and
Normalization convolution window is multiplied, and obtains the 2N-1 items of adding window, afterwards by the 1st and N+1 items, the 2nd and N+2 item ... N-1 items and
2N-1 items are added, and obtain the N point sequences through complete mutually pretreatment；
It is to eliminate non-caused by a variety of causes in the measurement of Fourier transform spectrometer, interference pattern that the purpose of the step is
Symmetry, ideally, bilateral sampled interference patterns are full symmetric in zero optical path difference point, and it is symmetrical from zero optical path difference to sample
Ground, equally spaced carry out.But, due to the presence of disturbing factor, sampled point deviates optical path difference position, causes bilateral sampling interferogram
Figure is asymmetric；For monolateral sampled interference patterns, do not start sampling from zero optical path difference, affect the accuracy for restoring spectrum；
Step 5：Zero padding；
On the basis of step 4 obtains data, Fourier transformation requires that it is 2 to calculate dataNIt is individual, and gather the dry of acquisition
Relate to diagram data to tend not to enough meet this condition, the interference pattern data padding that step 4 is obtained to 2NIt is individual；
Step 6：Fourier transformation；
The interference pattern processed through step 5 is converted to by spectrogram by Fourier transformation.
Embodiment 1
The present embodiment is specifically included：
1) data prediction：
Trend term is eliminated using least square method, and removes DC component, suppress random noise；
2) frequency filtering
The rectangular filter in spectrogram domain is generated for target wavelength, than being illustrated in figure 2 1000nm's to 1800nm
Wave filter, and Fourier's change is done to it, the wave filter in optical path difference-intensity domain is obtained, as shown in figure 3, with the wave filter pair
Data are filtered；
3) apodization：
Conventional window function includes quarter window, trapezoid window, Hanning windows, Blackman window, Gaussian function, Norton-
Beer functions etc.；
4) phasing
General method for correcting phase mainly has Connes root-squaring methods, Mertz product methods and Forman convolution methods etc.；
5) zero padding
Data volume is 1000 in this example, it is therefore desirable to by data padding to 1024；
6) Fourier transformation：
The interference pattern processed through step 5 is converted to by spectrogram by Fourier transformation；
Experimental result：
For the dextrorotation ripple of single-frequency, after introducing nonlinear noise, as a result as shown in figure 4, taking comprising frequency domain filtering
With not comprising the Fourier transform results after frequency domain filtering as shown in Figure 5 and Figure 6, wherein using target model after frequency domain filtering
Peak-to-peak contrast is that 19.96 peak-to-peak values 17.74 than not adopting frequency domain filtering have been higher by 12.5%, illustrates this method non-linear
The noise of reducing power under the influence of to(for) signal has preferably performance than conventional method.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, on the premise of without departing from the technology of the present invention principle, some improvement and deformation can also be made, these improve and deform
Also should be regarded as protection scope of the present invention.
Claims (3)
1. the efficient denoising method of a kind of fourier spectrometer, it is characterised in that the method comprises the steps：
Step 1：Data prediction；
The data of experiment are read from the detector of fourier spectrometer, and the data of acquisition are first by the side of LPF
Method removes DC component, and data afterwards eliminate trend term according to the mode of linear fit, then by the result for being obtained according to making an uproar
The statistical distribution of sound models to suppress random noise disturbance；
Step 2：Frequency domain filtering；
The data that step 1 is obtained, according to the target spectral coverage of instrument, by discrete cosine transform optical path difference-interference strength are generated
Interior wave filter, the data obtained to step 1 with the wave filter are filtered operation；
Step 3：Apodization；
The data obtained by step 2, by selecting suitable apodizing function, the data to being obtained are multiplied, according to number
Method according to statistics determines the parameter in algorithmic procedure, and the discontinuity at edge is relaxed by adding window, and the height for making secondary lobe becomes
Zero is bordering on, so that energy is relatively concentrated in main lobe, obtains being closer to real frequency spectrum；
Step 4：Phasing；
Used as input, the purpose of phasing constitutes first the Hanning window of a N point to data using step 3 acquisition；
Then Hanning window seeks convolution to oneself, obtains the convolution window of 2N-1 points, the sum of the convolution window of 2N-1 points is sought afterwards, by convolution
Each item of window obtains the normalization convolution window of 2N-1 points divided by the sum of convolution window, by the 1 of the data obtained：2N-1 items and normalizing
Change convolution window to be multiplied, obtain the 2N-1 items of adding window, afterwards by the 1st and N+1 items, the 2nd and N+2 items ... N-1 items and the
2N-1 items are added, and obtain the N point sequences through complete mutually pretreatment；
Step 5：Zero padding；
On the basis of step 4 obtains data, Fourier transformation requires that it is 2 to calculate dataNIt is individual, and gather the interference pattern for obtaining
Data tend not to enough meet this condition, the interference pattern data padding that step 4 is obtained to 2NIt is individual；
Step 6：Fourier transformation；
The interference pattern processed through step 5 is converted to by spectrogram by Fourier transformation.
2. the efficient denoising method of fourier spectrometer as claimed in claim 1, it is characterised in that apodizing function includes rectangle
Apodizing function.
3. the efficient denoising method of fourier spectrometer as claimed in claim 1, it is characterised in that window function includes triangle
Window, trapezoid window, Hanning windows, Blackman window, Gaussian function, Norton-Beer functions.
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| CN107610055A (en) * | 2017-07-28 | 2018-01-19 | 上海卫星工程研究所 | The noise measuring of Fourier transform spectrometer, interference pattern and suppressing method |
| CN107680063A (en) * | 2017-10-23 | 2018-02-09 | 西华大学 | A kind of Enhancement Method of direct digitization image |
| CN108181295A (en) * | 2018-01-24 | 2018-06-19 | 华南师范大学 | The identification of cosmic ray Spike and modification method in Raman spectroscopy data |
| CN111238644A (en) * | 2020-01-20 | 2020-06-05 | 西安工业大学 | White light interference removing method for interference spectrum of DFDI instrument |
| CN111289106A (en) * | 2020-03-26 | 2020-06-16 | 广西科技大学 | Spectral noise reduction method based on digital filtering |
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Cited By (9)
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| CN107610055A (en) * | 2017-07-28 | 2018-01-19 | 上海卫星工程研究所 | The noise measuring of Fourier transform spectrometer, interference pattern and suppressing method |
| CN107610055B (en) * | 2017-07-28 | 2020-09-15 | 上海卫星工程研究所 | Fourier transform spectrometer interferogram noise detection and suppression method |
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| CN111238644A (en) * | 2020-01-20 | 2020-06-05 | 西安工业大学 | White light interference removing method for interference spectrum of DFDI instrument |
| CN111238644B (en) * | 2020-01-20 | 2022-02-22 | 西安工业大学 | White light interference removing method for interference spectrum of DFDI instrument |
| CN111289106A (en) * | 2020-03-26 | 2020-06-16 | 广西科技大学 | Spectral noise reduction method based on digital filtering |
| CN111289106B (en) * | 2020-03-26 | 2022-11-11 | 广西科技大学 | Spectral noise reduction method based on digital filtering |
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