Signal filtering method and apparatus, and electronic device and computer program product

Abstract

Provided in the present application are a signal filtering method and apparatus, and an electronic device and a computer program product. The signal filtering method comprises: on the basis of a sparse constraint condition, performing noise reduction processing on a first temporal interferogram, which is output by a Fourier spectrometer, so as to obtain a second temporal interferogram, wherein the sparse constraint condition is used for constraining the deviation between the second temporal interferogram and the first temporal interferogram to be within a preset deviation range, and constraining the second temporal interferogram to achieve maximum sparsity without distortion; and on the basis of the second temporal interferogram, generating a spectrogram. Compared with an existing spectrogram which is obtained by means of directly performing Fourier transform on an interferogram, the embodiments of the present application can reduce noise which is present in a spectrogram, thereby improving the accuracy of the spectrogram.

Description

Signal filtering method and device, electronic equipment and computer program product TECHNICAL FIELD

[0001] The present application relates to the field of signal processing, in particular to a signal filtering method and device, electronic equipment and computer program product. BACKGROUND

[0002] In specific applications, the interferogram output by the Fourier spectrometer is often subjected to Fourier transform to obtain a spectrum.

[0003] However, in actual implementation, the internal structure of the Fourier spectrometer, such as the motion mechanism, or the acquisition circuit used for interference signal acquisition, often causes the interferogram output by the Fourier spectrometer to be affected by noise signals. Once the interferogram is introduced into noise, the spectrum obtained by subsequent Fourier transform of the interferogram will also have noise, and the accuracy of the spectrum is affected. SUMMARY

[0004] The present application provides a signal filtering method and device, electronic equipment and computer program product to reduce noise in the spectrum.

[0005] The present application provides a signal filtering method, which comprises: performing noise reduction processing on a first time sequence interferogram output by a Fourier spectrometer according to a sparse constraint condition to obtain a second time sequence interferogram; wherein the sparse constraint condition is used to constrain the deviation of the second time sequence interferogram from the first time sequence interferogram within a preset deviation range, and to constrain the sparsity of the second time sequence interferogram to be maximum under the premise of no distortion; and generating a spectrum according to the second time sequence interferogram.

[0006] Optionally, the sparse constraint condition is represented by a sparse constraint denoising optimization equation; the sparse constraint denoising optimization equation at least comprises: a fidelity term used to constrain the deviation of the second time sequence interferogram from the first time sequence interferogram within the preset deviation range; and a sparse constraint term used to constrain the sparsity of the second time sequence interferogram to be maximum under the premise of no distortion; wherein the noise reduction processing on the first time sequence interferogram output by the Fourier spectrometer according to the sparse constraint condition to obtain the second time sequence interferogram comprises: solving the sparse constraint denoising optimization equation according to a set solving algorithm to obtain the second time sequence interferogram.

[0007] Optionally, the sparse constraint denoising optimization equation is represented by the following formula: wherein, represents a second sequence composed of the light intensity values of each interference point in the second time sequence interferogram, represents the fidelity term; wherein, represents the sparse constraint term, the second time series interferogram is sparsest when is minimum the second time series interferogram is sparsest when

[0008] Optionally, the sparsity constraint term is represented by a first norm of a second sequence composed of the light intensity values of the interference points in the second time series interferogram.

[0009] Optionally, the sparsity constraint term is obtained by: performing total variation processing on the second sequence composed of the light intensity values of the interference points in the second time series interferogram to obtain a processing result, and representing the sparsity constraint term by a first norm or a second norm of the processing result.

[0010] Optionally, the fidelity term is represented by: wherein S represents a first sequence composed of the light intensity values of the interference points in the first time series interferogram; represents a second sequence composed of the light intensity values of the interference points in the second time series interferogram; represents a square of a second norm of a difference sequence obtained by subtracting the first sequence from the second sequence; wherein the is less than a value ε used to define the preset deviation range; when the deviation between the second time series interferogram and the first time series interferogram is within the preset deviation range.

[0011] Optionally, the method further comprises: training a sparse constraint denoising algorithm according to the sparsity constraint condition; wherein the trained sparse constraint denoising algorithm is used to perform denoising processing on an input time series interferogram and output a noise-free time series interferogram; the deviation between the output time series interferogram and the input time series interferogram is within the preset deviation range and the output time series interferogram is sparsest when not distorted; wherein the denoising processing of the first time series interferogram output by the Fourier spectrometer according to the sparsity constraint condition to obtain the second time series interferogram comprises: inputting the first time series interferogram output by the Fourier spectrometer to the sparse constraint denoising algorithm to obtain the second time series interferogram.

[0012] Optionally, the generation of the spectral graph according to the second time series interferogram comprises: performing equal optical path resampling processing on the second time series interferogram to obtain a processing result, and taking the processing result as the obtained sampling graph.

[0013] Optionally, the generating the spectrum map according to the second time sequence interferogram comprises: performing equal-optical-path resampling processing on the second time sequence interferogram to obtain a processing result, and performing truncation processing on the processing result to obtain a sampling map; wherein any two interference point pairs in the sampling map correspond to the same optical path difference value; any interference point pair comprises two adjacent interference points; the optical path difference value corresponding to any interference point pair is the difference between the optical path difference of one interference point and the optical path difference of the other interference point; the truncation processing is used to cut off each interference point not in a first set optical path difference range; the first set optical path difference range is related to the resolution of the Fourier spectrometer; and the spectrum map is generated according to the sampling map.

[0014] Optionally, the generating the spectrum map according to the sampling map comprises: performing phase correction processing on the sampling map to obtain a phase correction map; wherein the phase correction processing is used to align signals from different phases in the phase angle; performing apodization processing on the phase correction map to obtain an apodization map; wherein the apodization processing is used to cut off each interference point in the phase correction map not in a second set optical path difference range; performing zero padding processing on the apodization map to make the resolution of the processed apodization map meet a set requirement, and performing Fourier transform on the processed apodization map to generate the spectrum map.

[0015] The embodiment of the present application further provides a signal filtering device, which comprises: a noise reduction module, configured to perform noise reduction processing on a first time sequence interferogram output by a Fourier spectrometer according to a sparse constraint condition to obtain a second time sequence interferogram; wherein the sparse constraint condition is used to constrain the deviation between the second time sequence interferogram and the first time sequence interferogram to be within a preset deviation range, and constrain the sparsity of the second time sequence interferogram to be maximum on the premise of no distortion; and a spectrum map generation module, configured to generate a spectrum map according to the second time sequence interferogram.

[0016] The embodiment of the present application further provides an electronic device, which comprises: a processor; and a memory, in which computer program instructions are stored; the computer program instructions enable the processor to execute the steps in any of the above signal filtering methods when the computer program instructions are run by the processor.

[0017] The embodiment of the present application further provides a computer program product, which comprises a computer program; the computer program is executed by a processor to implement the signal filtering method of any of the above.

[0018] It can be seen from the above technical solution that, by means of the different sparse constraints of the interference signal and the noise signal, the first time sequence interference graph containing the interference signal and the noise signal output by the Fourier spectrometer is subjected to noise reduction processing, and then the second time sequence interference graph obtained after the noise reduction processing is subjected to Fourier transform to obtain the spectrum graph. Compared with the spectrum graph obtained by directly using the interference graph for Fourier transform, the noise existing in the spectrum graph can be reduced, and the accuracy of the spectrum graph can be improved.

[0019] Further, based on the description of the above sparse constraint condition, the sparse constraint condition restricts the deviation of the second time sequence interference graph from the first time sequence interference graph within a preset deviation range, so as to retain the light intensity values of each interference point in the first time sequence interference graph, avoid distortion of the second time sequence interference graph, and improve the accuracy of the spectrum graph obtained based on the second time sequence interference graph; and the sparse constraint condition also restricts the sparse degree of the second time sequence interference graph to be maximum under the premise of no distortion, so as to sufficiently filter the noise signal in the second time sequence interference graph, improve the signal-to-noise ratio of the second time sequence interference graph, and ensure the accuracy of the spectrum graph obtained based on the second time sequence interference graph. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are incorporated into and form a part of the specification, illustrate an embodiment consistent with the present specification and serve to explain the principles of the present specification together with the specification.

[0021] FIG. 1 is a flowchart of a signal filtering method provided by an embodiment of the present application.

[0022] FIG. 2 is an example diagram of a first time sequence interference graph provided by an embodiment of the present application.

[0023] FIG. 3 is an example diagram of a spectrum graph provided by an embodiment of the present application.

[0024] FIG. 4 is a flowchart of a spectrum graph recovery method provided by an embodiment of the present application.

[0025] FIG. 5 is an implementation schematic diagram of a spectrum graph recovery method provided by an embodiment of the present application.

[0026] FIG. 6 is a structural schematic diagram of a signal filtering device provided by an embodiment of the present application.

[0027] FIG. 7 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present application, and to make the above-mentioned purposes, features and advantages of the embodiments of the present application more apparent and easy to understand, the technical solutions in the embodiments of the present application will be further described in detail below with reference to the drawings.

[0029] Referring to FIG. 1, FIG. 1 is a flowchart of a signal filtering method provided by an embodiment of the present application. The method can be applied to an electronic device, such as a Fourier spectrometer, or other devices independent of the Fourier spectrometer, which is not particularly limited in the present application.

[0030] As shown in FIG. 1, the method can include the following steps S101-S102.

[0031] S101, performing denoising processing on a first time-series interferogram output by a Fourier spectrometer according to a sparsity constraint condition to obtain a second time-series interferogram; wherein the sparsity constraint condition is used to constrain the deviation of the second time-series interferogram from the first time-series interferogram within a preset deviation range, and to constrain the sparsity of the second time-series interferogram to be maximum under the premise of no distortion.

[0032] For ease of description, the time-series interferogram output by the Fourier spectrometer is referred to as the first time-series interferogram here. This is only for naming and is not intended to be limiting. Correspondingly, the above-mentioned second time-series interferogram is also only for naming for ease of description and is not intended to be limiting.

[0033] In a specific implementation, the sparsity of the interference signal and the noise signal is different. For example, the number of non-zero elements in the noise signal is relatively large, and the sparsity of the noise signal is relatively low; the number of non-zero elements in the interference signal is relatively small, and the sparsity of the interference signal is relatively high. By virtue of the difference in sparsity between the interference signal and the noise signal, the sparsity constraint condition is set to perform denoising processing on the first time-series interferogram (containing the interference signal and the noise signal) output by the Fourier spectrometer.

[0034] The sparsity constraint condition is not particularly limited in the embodiments of the present application. For example, the sparsity constraint condition is used to constrain the deviation of the second time-series interferogram from the first time-series interferogram within a preset deviation range when being set.

[0035] The above-mentioned preset deviation range is set according to actual requirements, for example, according to the requirement that the deviation of the second time-series interferogram from the first time-series interferogram is not large, to avoid distortion of the second time-series interferogram. As an embodiment, the above-mentioned deviation can be measured by the difference between the light intensity values of each interference point in the second time-series interferogram and the light intensity values of each interference point in the first time-series interferogram. How to determine the deviation between the light intensity values of each interference point in the second time-series interferogram and the light intensity values of each interference point in the first time-series interferogram will be described later, and will not be described here.

[0036] Further, the sparsity constraint condition in the embodiments of the present application is also used to constrain the sparsity of the second time-series interferogram to be maximum under the premise of no distortion.

[0037] As described above, the sparsity of the noise signal in the time sequence interference diagram is low, and the sparsity of the interference signal is high. Therefore, when the sparsity constraint condition is used to constrain the second time sequence interference diagram to reach the maximum sparsity without distortion, the noise signal in the second time sequence interference diagram is filtered to the maximum extent, the signal-to-noise ratio of the second time sequence interference diagram is improved, and the accuracy of the spectrum diagram determined based on the second time sequence interference diagram is improved.

[0038] For details of how to determine whether the sparsity of the second time sequence interference diagram reaches the maximum, the following will be described, which is not repeated here.

[0039] S102, generating a spectrum diagram according to the second time sequence interference diagram.

[0040] For details of how to generate a spectrum diagram according to the second time sequence interference diagram, the following will be described, which is not repeated here.

[0041] At this point, the process shown in FIG. 1 is completed.

[0042] As can be seen from the process shown in FIG. 1, the embodiments of the present application set a sparsity constraint condition by means of the different sparsities of the interference signal and the noise signal, to perform noise reduction processing on the first time sequence interference diagram output by the Fourier spectrometer and containing the interference signal and the noise signal, and then perform Fourier transform on the second time sequence interference diagram obtained after the noise reduction processing to obtain a spectrum diagram. Compared with the spectrum diagram obtained by directly performing Fourier transform on the interference diagram, the spectrum diagram can reduce the noise existing in the spectrum diagram and improve the accuracy of the spectrum diagram.

[0043] Further, based on the description of the sparsity constraint condition, the sparsity constraint condition constrains the deviation of the second time sequence interference diagram from the first time sequence interference diagram to be within a preset deviation range, to retain the light intensity values of the interference points in the first time sequence interference diagram, avoid distortion of the second time sequence interference diagram, and improve the accuracy of the spectrum diagram obtained based on the second time sequence interference diagram. In addition, the sparsity constraint condition also constrains the sparsity of the second time sequence interference diagram to reach the maximum without distortion, to sufficiently filter the noise signal in the second time sequence interference diagram, improve the signal-to-noise ratio of the second time sequence interference diagram, and ensure the accuracy of the spectrum diagram obtained based on the second time sequence interference diagram.

[0044] In addition, the type of the Fourier spectrometer in the present application includes but is not limited to a near-infrared spectrometer, a mid-infrared spectrometer, a long-infrared spectrometer, and a Fourier Raman spectrometer. The present application provides a general signal filtering method capable of improving the signal-to-noise ratio of the spectrum diagram obtained based on the interference diagram.

[0045] The following describes an implementation manner of performing noise reduction processing on the first time sequence interference diagram output by the Fourier spectrometer according to the sparsity constraint condition in S101 to obtain the second time sequence interference diagram.

[0046] As an optional embodiment, the sparse constraint condition can be set.

[0047] As an optional embodiment, the sparse constraint condition is represented by a sparse constraint denoising optimization equation.

[0048] The sparse constraint denoising optimization equation at least includes a fidelity term and a sparse constraint term; wherein the fidelity term is used to constrain the deviation of the second time sequence interferogram from the first time sequence interferogram within a preset deviation range; and the sparse constraint term is used to constrain the maximum sparsity of the second time sequence interferogram under the premise of no distortion.

[0049] According to the sparse constraint condition, the first time sequence interferogram output by the Fourier spectrometer is subjected to denoising processing to obtain the second time sequence interferogram, including: solving the sparse constraint denoising optimization equation according to a set solving algorithm to obtain the second time sequence interferogram.

[0050] In this embodiment, the sparse constraint condition is represented by a sparse constraint denoising optimization equation. The above-mentioned fidelity term can be represented as wherein, represents a second sequence composed of the light intensity values of each interference point in the second time sequence interferogram. The above-mentioned fidelity term is an expression related to the second sequence. For the specific form of expression, it will be described later, and will not be described here in detail. The above-mentioned preset deviation range can be set according to actual needs, and the present application does not make special limitations on this.

[0051] The above-mentioned sparse constraint term can be represented as is an expression related to the second sequence. As an embodiment, the sparsity of the second time sequence interferogram is negatively related to the sparsity of the second time sequence interferogram, so when takes the minimum value , the sparsity of the second time sequence interferogram is maximum under the premise of no distortion. For the specific form of expression, it will be described later, and will not be described here in detail.

[0052] After the above-mentioned sparse constraint denoising optimization equation is determined, the sparse constraint denoising optimization equation can be solved according to a set solving algorithm to obtain the second time sequence interferogram. The above-mentioned set solving algorithm includes but is not limited to convex optimization algorithm, non-convex optimization algorithm and greedy algorithm, and the present application does not make special limitations on this.

[0053] For example, when solving the above sparse constraint denoising optimization equation using a convex optimization algorithm, the above fidelity term and sparse constraint term are converted into an optimization equation, and a convex optimization algorithm such as gradient descent method or conjugate gradient method is selected to determine the second sequence when the stop condition of the convex optimization algorithm is met. The value of each element in the second sequence is the light intensity value corresponding to each interference point included in the second time sequence interferogram, so the second time sequence interferogram can be determined based on the second sequence.

[0054] The specific form of the fidelity term is described below.

[0055] As an embodiment, the above fidelity term can be expressed as:

[0056] where S represents a first sequence composed of light intensity values of each interference point in the first time sequence interferogram, represents a second sequence composed of light intensity values of each interference point in the second time sequence interferogram. represents the square of the second norm of the difference sequence, where the difference sequence is obtained by subtracting the first sequence from the second sequence.

[0057] wherein is less than a value ε used to define a preset deviation range; wherein when , it indicates that the deviation of the second time sequence interferogram from the first time sequence interferogram is within the preset deviation range.

[0058] The first sequence is composed of light intensity values of each interference point in the first time sequence interferogram, and the second sequence is composed of light intensity values of each interference point in the second time sequence interferogram. Here, it is only for naming and not for limitation. Referring to FIG. 2, which is an example diagram of a first time sequence interferogram provided by an embodiment of the present application. In FIG. 2, the horizontal axis represents optical path difference, and the vertical axis represents light intensity. Taking the five interference points with optical path differences of -2, -1, 0, 1 and 2 shown in FIG. 2 as an example, the first sequence composed of light intensity values of the five interference points is (0, 0, 0.7, 0, 0).

[0059] The difference sequence is obtained by subtracting the first sequence from the second sequence. For example, the number of light intensity values included in the first sequence is equal to the number of light intensity values included in the second sequence, and the difference sequence is specifically obtained by subtracting the light intensity values at the same positions in the first sequence and the second sequence.

[0060] For example, the first sequence and the second sequence are both one-dimensional vectors composed of N light intensity values, that is, S ∈ R N , Wherein, R represents a vector. The number of interference points included in the first time sequence interference diagram and the number of interference points included in the second time sequence interference diagram are both N. The first element of the difference sequence is a difference value obtained by subtracting the first light intensity value in the second sequence from the first light intensity value in the first sequence, and the Nth element of the difference sequence is a difference value obtained by subtracting the Nth light intensity value in the second sequence from the Nth light intensity value in the first sequence.

[0061] The fidelity term in the sparse constraint denoising optimization equation is the square of the second order norm of the difference sequence, that is, The square of the second order norm of the difference sequence is the sum of squares of the difference values obtained by subtracting the light intensity values at the same positions in the second sequence from the light intensity values at the same positions in the first sequence. The square of the second order norm of the difference sequence can be used to determine the deviation of the light intensity values of each interference point in the second time sequence interference diagram from the light intensity values of each interference point in the first time sequence interference diagram. When the square of the second order norm of the difference sequence is smaller, the deviation of the light intensity values of each interference point in the second time sequence interference diagram from the light intensity values of each interference point in the first time sequence interference diagram is smaller, and the deviation of the second time sequence interference diagram from the first time sequence interference diagram is also smaller.

[0062] Therefore, the fidelity term in the sparse constraint denoising optimization equation is expressed as follows:

[0063] Wherein, ε is a value used to limit the preset deviation range, and the value of ε can be set according to actual requirements. When , it indicates that the deviation of the second time sequence interference diagram from the first time sequence interference diagram is within the preset deviation range, achieving the purpose of constraining the second time sequence interference diagram to be distortion-free through the fidelity term.

[0064] The specific form of the sparse constraint term is described below.

[0065] Embodiments of the present application consider that although the zero norm of the second sequence corresponding to the second time sequence interference diagram can be used to measure the sparsity of the second time sequence interference diagram, the zero norm of the second sequence cannot be solved, and therefore the present application provides the following three ways to express the sparse constraint term .

[0066] As an embodiment, the sparse constraint term is represented by the first order norm of the second sequence composed of the light intensity values of each interference point in the second time sequence interference diagram, that is, the sparse constraint term is

[0067] The first norm of the second sequence is the sum of the intensity values in the second sequence. In the embodiments of the present application, the first norm of the second sequence is introduced as the sparse constraint term. When the sparse constraint term, i.e., the first norm of the second sequence, is the minimum, the sparsity of the second sequence reaches the maximum, the sparsity of the second time sequence interference diagram corresponding to the second sequence reaches the maximum, and thus the noise signals with low sparsity are filtered out to the maximum extent, and the accuracy of the spectrum diagram determined based on the second time sequence interference diagram is improved.

[0068] As an embodiment, the sparse constraint term can also be obtained by the following steps: performing total variation processing on the second sequence to obtain a processing result, and using the first norm of the processing result as the sparse constraint term. That is, the sparse constraint term can be represented as TV (second sequence), wherein TV represents total variation processing.

[0069] The total variation processing in the above embodiments can also be referred to as total variation difference processing, and is mainly used to describe the gradient change of the second sequence. TV (second sequence) represents the processing result obtained by performing total variation processing on the second sequence, TV (second sequence) represents the first norm of the processing result obtained by performing total variation processing on the second sequence, Specifically, it refers to the sum of the squares of the elements included in

[0070] In the embodiments of the present application, the second norm of the processing result obtained by performing total variation processing on the second sequence is introduced as the sparse constraint term. By minimizing the sparsity of the second sequence can reach the maximum, the sparsity of the second time sequence interference diagram corresponding to the second sequence reaches the maximum, and thus the noise signals with low sparsity are filtered out to the maximum extent, and the accuracy of the spectrum diagram determined based on the second time sequence interference diagram is improved.

[0071] As an embodiment, the sparse constraint term can also be obtained by the following steps: performing total variation processing on the second sequence to obtain a processing result, and using the second norm of the processing result as the sparse constraint term. That is, the sparse constraint term can be represented as TV (second sequence), wherein TV represents total variation processing.

[0072] The total variation processing in the above embodiments can also be referred to as total variation difference processing, and is mainly used to describe the gradient change of the second sequence. TV (second sequence) represents the processing result obtained by performing total variation processing on the second sequence, TV (second sequence) represents the second norm of the processing result obtained by performing total variation processing on the second sequence, The determination step of the second norm of the processing result is as follows: the square of each element included in is squared; and the square root of the result obtained by adding the squares of the elements included in is obtained.

[0073] In the embodiments of the present application As a sparse constraint term, by minimizing The sparsity of the second sequence can be maximized, the sparsity of the second time sequence interferogram corresponding to the second sequence is maximized, so that the noise signal with low sparsity is filtered out to the greatest extent, and the accuracy of the spectrum map determined based on the second time sequence interferogram is improved.

[0074] The following describes another implementation of the method of S101 for performing noise reduction processing on the first time sequence interferogram output by the Fourier spectrometer according to the sparse constraint condition.

[0075] As an optional embodiment, before step S101, the method further includes: training a sparse constraint denoising algorithm according to the sparse constraint condition; wherein the trained sparse constraint denoising algorithm is used to perform noise reduction processing on the input time sequence interferogram and output a noise-free time sequence interferogram; the deviation of the output time sequence interferogram from the input time sequence interferogram is within a preset deviation range, and the output time sequence interferogram has the maximum sparsity under the premise of no distortion.

[0076] In the present embodiment, the sparse constraint denoising algorithm is trained according to the sparse constraint condition in advance before the noise reduction processing on the first time sequence interferogram. When the sparse constraint denoising algorithm performs noise reduction processing on the input time sequence interferogram, it is also subject to the constraint of the sparse constraint condition, that is, the deviation of the output time sequence interferogram from the input time sequence interferogram is within a preset deviation range, and the output time sequence interferogram has the maximum sparsity under the premise of no distortion.

[0077] As an embodiment, the time sequence interferogram and the first noise-free time sequence interferogram corresponding to the time sequence interferogram can be obtained in advance as training samples. The above-mentioned time sequence interferogram is input into the sparse constraint denoising algorithm to obtain the second noise-free time sequence interferogram output by the sparse constraint denoising algorithm. Whether the deviation between the second noise-free time sequence interferogram and the first noise-free time sequence interferogram meets the requirements is determined by a loss function, for example, when it is determined that the loss function value of the second noise-free time sequence interferogram and the first noise-free time sequence interferogram is less than a preset threshold, it is determined that the second noise-free time sequence interferogram output by the sparse constraint denoising algorithm meets the requirements. The above-mentioned preset threshold can be set according to actual needs, and the present application does not limit this.

[0078] The above-mentioned sparse constraint denoising algorithm includes but is not limited to machine learning algorithms such as dictionary learning, folding learning, and Gaussian process learning, and deep learning algorithms such as convolutional neural network, Transformer, graph neural network, and spiking neural network, and the present application does not particularly limit this.

[0079] The sparse constraint denoising algorithm is used for denoising the input time sequence interferogram and outputting a noise-free time sequence interferogram. The algorithm can ensure that the deviation of the output time sequence interferogram from the input time sequence interferogram is within a preset deviation range (i.e., ensuring that the output time sequence interferogram is not distorted), and the sparsity of the output time sequence interferogram reaches the maximum under the premise of not being distorted (i.e., ensuring that the noise signals in the input time sequence interferogram are sufficiently filtered out), thereby realizing the denoising of the time sequence interferogram, improving the signal-to-noise ratio of the denoised time sequence interferogram, and further improving the accuracy of the spectral graph determined based on the denoised time sequence interferogram.

[0080] On this basis, the first time sequence interferogram output by the Fourier spectrometer is denoised according to the sparse constraint condition in S101 to obtain a second time sequence interferogram, including: inputting the first time sequence interferogram output by the Fourier spectrometer into the sparse constraint denoising algorithm to obtain the second time sequence interferogram.

[0081] When the first time sequence interferogram is denoised, the first time sequence interferogram is input into the sparse constraint denoising algorithm, and the output of the sparse constraint denoising algorithm is the denoised second time sequence interferogram. Essentially, the first time sequence interferogram is also denoised according to the sparse constraint condition, which can also ensure that the noise signals in the first time sequence interferogram are sufficiently filtered out, improve the signal-to-noise ratio of the second time sequence interferogram, and further improve the accuracy of the spectral graph determined based on the second time sequence interferogram.

[0082] The generation of the spectral graph according to the second time sequence interferogram in S102 is described below.

[0083] Referring to FIG. 5, FIG. 5 is a flowchart of a spectral graph recovery method (a method for generating a spectral graph according to a second time sequence interferogram) provided by an embodiment of the present application. As shown in FIG. 5, the flowchart includes the following steps S501-S504.

[0084] S501, the second time sequence interferogram is processed by equal optical path resampling to obtain a processing result, and the processing result is processed by truncation to obtain a sampling graph; the optical path difference values corresponding to any two interference point pairs in the sampling graph are the same; any interference point pair includes two adjacent interference points; the optical path difference value corresponding to any interference point pair is the difference between the optical path difference of one interference point and the optical path difference of the other interference point; the truncation processing is used to cut off each interference point not within a first set optical path difference range. The first set optical path difference range is related to the resolution of the Fourier spectrometer, wherein the resolution of the Fourier spectrometer refers to the smallest difference between two wavelengths that can be distinguished by the Fourier spectrometer. For example, the first set optical path difference range is positively correlated with the resolution of the Fourier spectrometer.

[0085] In consideration of the fact that the Fourier spectrometer actually collects the interference signals according to preset time intervals, that is, the interference points in the interference pattern are time intervals, but the movement speed of the movement mechanism in the Fourier spectrometer fluctuates, and it cannot be guaranteed that the optical path difference values corresponding to any two interference points in the interference pattern are equal.

[0086] In another embodiment, the second time sequence interference pattern can be subjected to equal optical path resampling processing to obtain a processing result, and the processing result can be directly taken as a sampling pattern; wherein the optical path difference values corresponding to any two interference point pairs in the sampling pattern are equal; wherein any interference point pair comprises two adjacent interference points; and the optical path difference value corresponding to any interference point pair is the difference between the optical path difference of one of the interference points and the optical path difference of the other interference point.

[0087] Therefore, the embodiment of the present application first subjects the second time sequence interference pattern to equal optical path resampling processing, so that the optical path difference values corresponding to any two interference point pairs in the second time sequence interference pattern after the equal optical path resampling processing are equal. The above-mentioned any interference point pair comprises two adjacent interference points. The optical path difference value corresponding to any interference point pair is the difference between the optical path difference of one of the interference points and the optical path difference of the other interference point. The accuracy of the subsequently generated spectral pattern is further improved by subjecting the second time sequence interference pattern to equal optical path resampling processing.

[0088] The equal optical path resampling processing mode includes but is not limited to the nearest neighbor interpolation method, the linear interpolation method, the polynomial interpolation method, the cubic spline curve interpolation method, the β-spline curve interpolation method, etc., which are not particularly limited in the present application.

[0089] Taking the equal optical path resampling processing by using the nearest neighbor interpolation method as an example, a set optical path difference value is determined in advance, and each new optical path difference corresponding to the horizontal axis of the second time sequence interference pattern is determined according to the first set optical path difference range and the set optical path difference value. The light intensity value of the interference point corresponding to each new optical path difference can be determined by the following method: taking the light intensity value of the interference point corresponding to the optical path difference closest to the new optical path difference in the second time sequence interference pattern (that is, the optical path difference value closest to the new optical path difference) as the light intensity value of the interference point corresponding to the new optical path difference. This method can retain the original light intensity value of the interference point in the second time sequence interference pattern, and does not involve complex interpolation calculation, and can obtain the equal optical path resampled interference pattern more quickly.

[0090] Referring to FIG. 4, which is a flowchart of a spectral pattern recovery method provided by an embodiment of the present application, when S401 in FIG. 4 is implemented, is taken as the sparse constraint term in the sparse constraint denoising optimization equation, and The near-infrared interference pattern is subjected to denoising processing to obtain a second time sequence interference pattern. The specific process of the denoising processing is described above, and will not be described here again.

[0091] When S402 in FIG. 4 is implemented, the second time sequence interferogram is resampled at equal optical path lengths by using a linear interpolation method. For the light intensity value of the interference point corresponding to each new optical path difference, the light intensity value of the interference point corresponding to the new optical path difference is determined by using the two known interference points with the light intensity values closest to the new optical path difference (i.e., the optical path difference value of the new optical path difference is the smallest). This method can obtain a smoother sampling diagram.

[0092] Further, the processing result of the second time sequence interferogram after the equal optical path length resampling processing is further truncated to obtain a sampling diagram, so as to remove redundant interference points. The above-mentioned redundant interference points are the interference points not in the first set optical path difference range. As an embodiment, the first set optical path difference range can be determined according to the resolution of the Fourier spectrometer, and the specific determination process is not limited in the embodiment.

[0093] S502, performing phase correction processing on the sampling diagram to obtain a phase correction diagram; wherein the phase correction processing is used to align the signals from different phases in the phase angle.

[0094] The embodiment of the present application also performs phase correction processing on the sampling diagram to obtain a phase correction diagram, so as to align the signals from different phases in the phase angle, correct the zero offset of the sampling diagram, and ensure the accuracy of the subsequent recovered spectral diagram.

[0095] As an embodiment, when the phase correction processing is performed, the interference point corresponding to the zero optical path difference in the sampling diagram is moved from the current position to the position with the optical path difference of zero by adjusting the position of the sampling diagram. The horizontal axis of the sampling diagram represents the optical path difference, and the vertical axis represents the light intensity value. The entire sampling diagram is moved in the direction of the horizontal axis of the sampling diagram, so that the horizontal coordinate of the interference point corresponding to the zero optical path difference in the sampling diagram is 0. For example, the interference point under the zero optical path difference collected by the Fourier spectrometer is the first interference point, that is, the interference point corresponding to the zero optical path difference is the first interference point. However, in the sampling diagram, the current position A of the first interference point corresponds to the optical path difference which is not zero. In order to ensure the accuracy of the subsequent recovered spectral diagram, the first interference point needs to be moved from the current position A to the position B with the optical path difference of zero.

[0096] The method for performing the above-mentioned phase correction processing includes but is not limited to the power spectrum method, the Mertz phase correction method, the Forman phase correction method, etc., and the embodiment of the present application does not make special limitation thereto. Referring to FIG. 4, when S403 in FIG. 4 is implemented, the power spectrum method is used to perform the phase correction on the sampling diagram, and the specific implementation process of the power spectrum method for performing the phase correction is not described herein.

[0097] S503, apodize the phase correction graph to obtain an apodized graph; wherein the apodization is configured to cut off each interference point in the phase correction graph that is not within a second set optical path difference range.

[0098] The embodiment of the present application also apodizes the phase correction graph to obtain an apodized graph, so as to cut off each interference point in the phase correction graph that is not within a second set optical path difference range, thereby suppressing the sidelobe of the apodized graph and reducing the frequency domain leakage. The second set optical path difference range can be set according to actual requirements, and the present application does not make special limitations.

[0099] As an embodiment, the phase correction sequence can be composed based on the light intensity values of each interference point in the phase correction graph, and the sequence obtained by multiplying the phase correction sequence by an apodization function is taken as an apodization sequence. The window range of the apodization function corresponds to the second set optical path difference range. The apodization function retains the elements in the phase correction sequence within the window range and sets the elements in the phase correction sequence outside the window range to 0. The data included in the apodization sequence is the light intensity value of each interference point in the apodized graph, and the apodized graph can be obtained based on the apodization sequence.

[0100] The apodization function includes but is not limited to a rectangular window, a triangular window, a cosine window, a Hamming window, a Hanning window, a Blackman-Harris window, a Norton-Beer window, etc.

[0101] Referring to FIG. 4, when S404 in FIG. 4 is implemented, a rectangular window is used as an apodization function to apodize the phase correction graph. The rectangular window retains the values in the phase correction sequence within the window range and changes the values in the phase correction sequence outside the window range to 0. The window range corresponds to the second set optical path difference range.

[0102] S504, apodize the phase correction graph to obtain an apodized graph; wherein the apodization is configured to cut off each interference point in the phase correction graph that is not within a second set optical path difference range.

[0103] The embodiment of the present application also apodizes the phase correction graph to obtain an apodized graph, so as to cut off each interference point in the phase correction graph that is not within a second set optical path difference range, thereby suppressing the sidelobe of the apodized graph and reducing the frequency domain leakage. The second set optical path difference range can be set according to actual requirements, and the present application does not make special limitations.

[0104] Finally, the apodization-processed apodization graph is subjected to Fourier transform to obtain a spectrum graph. Referring to FIG. 3, which is an example of a spectrum graph provided by an embodiment of the present application, the horizontal axis of the spectrum graph recovered from the second time-series interference graph represents the wave number, and the vertical axis of the spectrum graph represents the light intensity.

[0105] Referring to FIG. 4, after the near-infrared interference graph is subjected to noise reduction processing, equal-path resampling, phase correction, apodization processing, and zero padding, the Fourier transform in S406 is performed to recover the near-infrared spectrum graph, and the accuracy of the near-infrared spectrum graph recovered through the process shown in FIG. 4 is higher.

[0106] At this point, the process shown in FIG. 5 is completed.

[0107] Referring to FIG. 6, which is a structural schematic diagram of a signal filtering device 600 provided by an embodiment of the present application, the device includes a noise reduction module 601 and a spectrum graph generation module 602.

[0108] The noise reduction module 601 is configured to perform noise reduction processing on the first time-series interference graph output by the Fourier spectrometer according to the sparse constraint condition to obtain a second time-series interference graph; wherein the sparse constraint condition is used to constrain the deviation of the second time-series interference graph from the first time-series interference graph to be within a preset deviation range, and to constrain the sparsity of the second time-series interference graph to be maximum under the premise of no distortion.

[0109] The spectrum graph generation module 602 is configured to generate a spectrum graph according to the second time-series interference graph.

[0110] As an optional embodiment, the device further includes an algorithm training module configured to train a sparse constraint denoising algorithm according to the sparse constraint condition; wherein the trained sparse constraint denoising algorithm is used to perform noise reduction processing on an input time-series interference graph and output a noise-free time-series interference graph; the deviation of the output time-series interference graph from the input time-series interference graph is within a preset deviation range, and the sparsity of the output time-series interference graph is maximum under the premise of no distortion; and the noise reduction module is specifically configured to input the first time-series interference graph output by the Fourier spectrometer to the sparse constraint denoising algorithm to obtain the second time-series interference graph.

[0111] As an optional embodiment, the sparse constraint condition is represented by a sparse constraint denoising optimization equation; the sparse constraint denoising optimization equation at least includes a fidelity term and a sparse constraint term; wherein the fidelity term is used to constrain the deviation of the second time-series interference graph from the first time-series interference graph to be within a preset deviation range; and the sparse constraint term is used to constrain the sparsity of the second time-series interference graph to be maximum under the premise of no distortion; and the noise reduction module is specifically configured to solve the sparse constraint denoising optimization equation according to a set solving algorithm to obtain the second time-series interference graph.

[0112] As an optional embodiment, the sparse constraint denoising optimization equation is expressed by the following formula:

[0113] wherein, S represents a first sequence composed of light intensity values of each interference point in the first time series interferogram; represents a second sequence composed of light intensity values of each interference point in the second time series interferogram; represents a fidelity term for constraining the deviation of the second time series interferogram from the first time series interferogram within a preset deviation range; represents a sparse constraint term, is negatively related to the sparsity of the second time series interferogram, and when the minimum value is taken , the sparsity of the second time series interferogram is maximum under the premise of no distortion.

[0114] As an optional embodiment, the sparse constraint term is expressed by the first order norm of the second sequence composed of light intensity values of each interference point in the second time series interferogram.

[0115] As an optional embodiment, the sparse constraint term is obtained by the following steps: full variation processing is performed on the second sequence composed of light intensity values of each interference point in the second time series interferogram to obtain a processing result, and the first order norm or the second order norm of the processing result is used to express the sparse constraint term.

[0116] As an optional embodiment, the fidelity term is expressed by the following formula:

[0117] wherein, S represents a first sequence composed of light intensity values of each interference point in the first time series interferogram; represents a second sequence composed of light intensity values of each interference point in the second time series interferogram; represents the square of the second order norm of the difference sequence; the difference sequence is obtained by subtracting the second sequence from the first sequence; wherein, the is less than a value ε used to define the preset deviation range; wherein, when , it indicates that the deviation of the second time series interferogram from the first time series interferogram is within the preset deviation range, and ε is a value used to define the preset deviation range.

[0118] As an optional embodiment, the spectrum map generation module is specifically configured to: perform equal optical path resampling processing on the second time series interferogram to obtain a processing result, and take the processing result as the sampling map.

[0119] As an optional embodiment, the spectrum generation module is specifically configured to: perform equal-optical-path resampling on the second time sequence interferogram to obtain a processing result, and perform truncation processing on the processing result to obtain a sampling graph; wherein any two interference point pairs in the sampling graph correspond to the same optical path difference value; wherein any interference point pair comprises two adjacent interference points; the optical path difference value corresponding to any interference point pair is the difference between the optical path difference of one of the interference points and the optical path difference of the other interference point; the truncation processing is configured to cut off each interference point not in a first set optical path difference range; the first set optical path difference range is related to the resolution of the Fourier spectrometer; and the spectrum is generated according to the sampling graph.

[0120] As an optional embodiment, the spectrum generation module is specifically configured to: perform phase correction processing on the sampling graph to obtain a phase correction graph; wherein the phase correction processing is configured to align signals from different phases in a phase angle; perform apodization processing on the phase correction graph to obtain an apodization graph; wherein the apodization processing is configured to cut off each interference point in the phase correction graph not in a second set optical path difference range; perform zero padding processing on the apodization graph to make the resolution of the processed apodization graph meet a set requirement, and perform Fourier transform on the processed apodization graph to generate the spectrum.

[0121] For detailed description of the signal filtering device provided in the present application, please refer to the embodiments of the signal filtering method described above, which will not be repeated here.

[0122] Please refer to FIG. 7, which is a structural schematic diagram of an electronic device provided in an embodiment of the present application. The electronic device comprises: a processor 701; and a memory 702, wherein the memory 702 stores computer program instructions, and the computer program instructions make the processor 701 execute the steps in any of the above signal filtering methods when the computer program instructions are run by the processor 701.

[0123] For detailed description of the electronic device provided in the present application, please refer to the embodiments of the signal filtering method described above, which will not be repeated here.

[0124] The embodiments of the present application further provide a computer program product comprising a computer program, which, when executed by a processor, implements any of the above signal filtering methods.

[0125] For detailed description of the computer program product provided in the embodiments of the present application, please refer to the embodiments of the signal filtering method described above, which will not be repeated here.

[0126] In this application, the terms "comprise", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0127] The above description of disclosed embodiments enables one of ordinary skill in the art to make or use the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A signal filtering method, comprising: performing denoising processing on a first time series interferogram output by a Fourier spectrometer according to a sparsity constraint condition to obtain a second time series interferogram, wherein the sparsity constraint condition is used to constrain a deviation of the second time series interferogram from the first time series interferogram to be within a preset deviation range and to constrain the second time series interferogram to have a maximum sparsity on a premise of no distortion; generating a spectrum map according to the second time series interferogram.

2. The signal filtering method of claim 1, wherein, The sparsity constraint condition is represented by a sparsity constraint denoising optimization equation, and the sparsity constraint denoising optimization equation at least includes: a fidelity term used to constrain the deviation of the second time series interferogram from the first time series interferogram to be within the preset deviation range; and a sparsity constraint term used to constrain the second time series interferogram to have a maximum sparsity on the premise of no distortion. The performing denoising processing on the first time series interferogram output by the Fourier spectrometer according to the sparsity constraint condition to obtain the second time series interferogram includes solving the sparsity constraint denoising optimization equation according to a set solving algorithm to obtain the second time series interferogram.

3. The signal filtering method according to claim 2, characterized by, The sparse constraint denoising optimization equation is expressed by the following formula: wherein a second sequence composed of light intensity values of each interference point in the second time series interferogram, the fidelity term is represented by the first sequence composed of light intensity values of each interference point in the first time series interferogram, the sparsity constraint term is represented by a first norm of the second sequence composed of light intensity values of each interference point in the second time series interferogram, wherein negatively related to the sparsity of the second time series interferogram, when taking the minimum value the second time series interferogram has the maximum sparsity on the premise of no distortion.

4. The signal filtering method according to claim 2 or 3, characterized by, The sparsity constraint term is represented by the first norm of the second sequence composed of light intensity values of each interference point in the second time series interferogram.

5. The signal filtering method according to claim 2 or 3, characterized by, The sparsity constraint term is obtained by the following steps: performing total variation processing on the second sequence composed of light intensity values of each interference point in the second time series interferogram to obtain a processing result, the sparsity constraint term is represented by a first norm or a second norm of the processing result.

6. The signal filtering method according to any one of claims 2 to 5, characterized in that, the fidelity term is represented by the following formula: wherein S represents a first sequence composed of light intensity values of each interference point in the first time series interferogram; a second sequence composed of light intensity values of each interference point in the second time series interferogram; a square of a second norm of a difference sequence obtained by subtracting the second sequence from the first sequence; Among them, the less than a value ε used to define the preset deviation range; when the deviation of the second time series interferogram from the first time series interferogram is within the preset deviation range.

7. The signal filtering method according to any one of claims 1 to 6, characterized by, The method further includes: training a sparsity constraint denoising algorithm according to the sparsity constraint condition; wherein the trained sparsity constraint denoising algorithm is used to perform denoising processing on an input time series interferogram and output a noise-free time series interferogram, the deviation of the output time series interferogram from the input time series interferogram is within the preset deviation range, and the output time series interferogram has a maximum sparsity on the premise of no distortion; wherein the performing denoising processing on the first time series interferogram output by the Fourier spectrometer according to the sparsity constraint condition to obtain the second time series interferogram includes inputting the first time series interferogram output by the Fourier spectrometer to the sparsity constraint denoising algorithm to obtain the second time series interferogram.

8. The signal filtering method according to any one of claims 1 to 7, characterized by, The generating a spectrum map according to the second time series interferogram includes: The second time sequence interferogram is subjected to equal optical path length resampling processing to obtain a processing result, and the processing result is taken as a sampling graph; and a spectrum graph is generated according to the sampling graph.

9. The signal filtering method according to any one of claims 1 to 7, characterized by, The generating the spectrum graph according to the second time sequence interferogram comprises: The second time sequence interferogram is subjected to equal optical path length resampling processing to obtain a processing result, and the processing result is subjected to truncation processing to obtain a sampling graph; wherein the optical path difference values corresponding to any two interference point pairs in the sampling graph are the same; wherein any interference point pair comprises two adjacent interference points; the optical path difference value corresponding to any interference point pair is the difference between the optical path difference of one interference point and the optical path difference of the other interference point; the truncation processing is used to cut off each interference point not in a first set optical path difference range; the first set optical path difference range is related to the resolution of the Fourier spectrometer; The spectrum graph is generated according to the sampling graph.

10. The signal filtering method according to claim 8 or 9, characterized by, The generating the spectrum graph according to the second time sequence interferogram comprises: The sampling graph is subjected to phase correction processing to obtain a phase correction graph; wherein the phase correction processing is used to align signals from different phases in the phase angle; The phase correction graph is subjected to apodization processing to obtain an apodization graph; wherein the apodization processing is used to cut off each interference point in the phase correction graph not in a second set optical path difference range; The apodization graph is subjected to zero padding processing to make the resolution of the processed apodization graph meet a set requirement, and the processed apodization graph is subjected to Fourier transform to generate the spectrum graph.

11. A signal filtering device, characterized by The device comprises: The noise reduction module is configured to perform noise reduction processing on a first time sequence interferogram output by a Fourier spectrometer according to a sparsity constraint condition to obtain a second time sequence interferogram; wherein the sparsity constraint condition is used to constrain the deviation between the second time sequence interferogram and the first time sequence interferogram to be within a preset deviation range, and to constrain the sparsity of the second time sequence interferogram to be maximum on the premise of no distortion; The spectrum graph generation module is configured to generate a spectrum graph according to the second time sequence interferogram.

12. The signal filtering device of claim 11, wherein, The sparsity constraint condition is represented by a sparsity constraint denoising optimization equation, and the sparsity constraint denoising optimization equation at least comprises: a fidelity term used to constrain the deviation between the second time sequence interferogram and the first time sequence interferogram to be within the preset deviation range; and a sparsity constraint term used to constrain the sparsity of the second time sequence interferogram to be maximum on the premise of no distortion; The noise reduction module is specifically configured to solve the sparsity constraint denoising optimization equation according to a set solving algorithm to obtain the second time sequence interferogram.

13. The signal filtering device of claim 12, wherein, The sparse constraint denoising optimization equation is represented by the following equation: wherein, a second sequence composed of the optical intensity values of the interference points in the second time sequence interferogram, the fidelity term is represented by the sparsity constraint term is represented by wherein negatively related to the sparsity of the second time series interferogram, when taking the minimum value the sparsity of the second time sequence interferogram is maximum on the premise of no distortion.

14. The signal filtering device according to claim 12 or 13, characterized in that, The sparsity constraint term is represented by the first order norm of the second sequence composed of the optical intensity values of the interference points in the second time sequence interferogram.

15. The signal filtering device according to claim 12 or 13, characterized in that, The sparsity constraint term is obtained by the following steps: the second sequence composed of the optical intensity values of the interference points in the second time sequence interferogram is subjected to total variation processing to obtain a processing result, the sparsity constraint term is represented by the first order norm or the second order norm of the processing result.

16. The signal filtering device according to any one of claims 12 to 15, characterized in that, the fidelity term is represented by the following formula: S represents a first sequence composed of light intensity values of each interference point in the first time series interferogram; S represents a second sequence composed of light intensity values of each interference point in the second time series interferogram; represents a square of a second norm of a difference sequence between the first sequence and the second sequence; the difference sequence is obtained by subtracting the first sequence from the second sequence; Among them, the less than a value ε used to define the preset deviation range; when When the deviation between the second time series interferogram and the first time series interferogram is within the preset deviation range.

17. The signal filtering device according to any one of claims 11 to 16, characterized in that, The device further comprises an algorithm training module, The algorithm training module is configured to train a sparse constraint denoising algorithm according to the sparse constraint condition; wherein the trained sparse constraint denoising algorithm is configured to perform denoising processing on an input time series interferogram and output a noise-free time series interferogram; the deviation between the output time series interferogram and the input time series interferogram is within the preset deviation range, and the output time series interferogram has the maximum sparsity on the premise of no distortion; The denoising module is specifically configured to input the first time series interferogram output by the Fourier spectrometer to the sparse constraint denoising algorithm to obtain the second time series interferogram.

18. The signal filtering device according to any one of claims 11 to 17, characterized in that, The spectrum generation module is specifically configured to: perform equal optical path resampling processing on the second time series interferogram to obtain a processing result, and use the processing result as a sampling graph; and generate a spectrum graph according to the sampling graph.

19. The signal filtering device according to any one of claims 11 to 17, characterized in that, The spectrum generation module is specifically configured to: perform equal optical path resampling processing on the second time series interferogram to obtain a processing result, and perform truncation processing on the processing result to obtain a sampling graph; wherein the optical path difference values of any two interference point pairs in the sampling graph are the same; wherein any interference point pair comprises two adjacent interference points; the optical path difference value corresponding to any interference point pair is the difference between the optical path difference of one interference point and the optical path difference of the other interference point; the truncation processing is configured to truncate each interference point not within a first set optical path difference range; the first set optical path difference range is related to the resolution of the Fourier spectrometer; generate a spectrum graph according to the sampling graph.

20. The signal filtering device of claim 18 or 19, wherein, The spectrum generation module is specifically configured to: perform phase correction processing on the sampling graph to obtain a phase correction graph; wherein the phase correction processing is configured to align signals from different phases in the phase angle; perform apodization processing on the phase correction graph to obtain an apodization graph; wherein the apodization processing is configured to truncate each interference point in the phase correction graph not within a second set optical path difference range; perform zero padding processing on the apodization graph to make the resolution of the processed apodization graph meet a set requirement, and perform Fourier transform on the processed apodization graph to generate the spectrum graph.

21. An electronic device, comprising: comprise: a processor; and a memory having computer program instructions stored therein, the computer program instructions, when executed by the processor, causing the processor to execute the signal filtering method of any one of claims 1 to 10.

22. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the signal filtering method of any one of claims 1 to 10. The computer program is executed by the processor to implement the signal filtering method of any one of claims 1 to 10.

Images