CN107907542B - IVMD and energy estimation combined DSPI phase filtering method - Google Patents
IVMD and energy estimation combined DSPI phase filtering method Download PDFInfo
- Publication number
- CN107907542B CN107907542B CN201711009315.2A CN201711009315A CN107907542B CN 107907542 B CN107907542 B CN 107907542B CN 201711009315 A CN201711009315 A CN 201711009315A CN 107907542 B CN107907542 B CN 107907542B
- Authority
- CN
- China
- Prior art keywords
- modal
- energy
- dspi
- components
- component
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000001914 filtration Methods 0.000 title claims abstract description 21
- 238000010587 phase diagram Methods 0.000 claims abstract description 55
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 37
- 230000003190 augmentative effect Effects 0.000 claims description 9
- 230000035772 mutation Effects 0.000 claims description 7
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 abstract description 8
- 230000008569 process Effects 0.000 abstract description 8
- 238000010586 diagram Methods 0.000 abstract description 7
- 238000005259 measurement Methods 0.000 abstract description 5
- 238000004364 calculation method Methods 0.000 description 4
- 239000002131 composite material Substances 0.000 description 4
- 238000005305 interferometry Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 230000014509 gene expression Effects 0.000 description 3
- 238000003384 imaging method Methods 0.000 description 3
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 229910052802 copper Inorganic materials 0.000 description 2
- 239000010949 copper Substances 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001427 coherent effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000009659 non-destructive testing Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
- G01B11/161—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge by interferometric means
- G01B11/162—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge by interferometric means by speckle- or shearing interferometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/41—Refractivity; Phase-affecting properties, e.g. optical path length
- G01N21/45—Refractivity; Phase-affecting properties, e.g. optical path length using interferometric methods; using Schlieren methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/10—Image enhancement or restoration using non-spatial domain filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Image Processing (AREA)
Abstract
A DSPI phase filtering method combining IVMD and energy estimation, the DSPI phase filtering method comprising the steps of: the method comprises the steps that the number of modes after decomposition is required to be preset in the decomposition process of the VMD method, the number of mode components after decomposition is estimated according to the length and the width of a phase diagram, and the optimal mode number is selected by utilizing an orthogonal index; carrying out variation modal decomposition on the DSPI phase diagram according to the number of the optimal modal quantity to obtain a series of modal function components, namely frequency modulation and amplitude modulation components; calculating the energy of the frequency modulation and amplitude modulation components, and estimating the energy of the noise components by using the energy of the decomposed components; and analyzing a curve change diagram of the energy logarithm value and the modal component number, extracting a noise-free component, and obtaining a filtered DSPI phase diagram. The method avoids the problem of setting the mode number in the VMD decomposition process on the premise of a large amount of noise contained in the speckle phase diagram, and adopts the IVMD to filter the image, remove noise interference and obtain accurate phase measurement information.
Description
Technical Field
The invention relates to the field of laser nondestructive testing and optical image processing, in particular to a DSPI (differential mode decomposition) phase filtering method combining IVMD (improved variable mode decomposition) and energy estimation.
Background
In recent years, with the development of aerospace, various composite materials have been widely used. However, the performance of the components is obviously reduced and the service time is reduced due to the defects of deformation, displacement and the like in the processing, manufacturing and service processes of the composite materials. Therefore, the composite materials need to be detected and evaluated, a scheme is provided for subsequent rush repair, and the healthy operation of aerospace equipment is guaranteed.
Research shows that when the surface of an object irradiated by coherent light is deformed or displaced, the deformation of the object surface is converted into the phase change of speckles on an imaging surface, and the phenomenon is called speckle interference. Digital speckle interference (DSPI) is a non-contact full-field measurement technique that can measure displacement, deformation, surface defects, etc. of composite materials. The acquired speckle phase image is interfered by a large amount of noise, so that the signal-to-noise ratio of the speckle phase image is low, the phase measurement sensitivity is low, and the accurate information of a measurement object cannot be obtained. Therefore, it is necessary to process the speckle phase map to improve the signal-to-noise ratio of the image and remove noise interference.
For DSPI phase noise reduction, scholars at home and abroad propose various noise reduction methods which are mainly classified into a space domain class and a time frequency analysis class. The space domain method mainly comprises a mean filtering method and a median filtering method: the mean filtering method also destroys the detail part of the image while filtering noise, so that the processed image becomes fuzzy, and information with abrupt phase change is lost; the median filtering method can retain the edge information while reducing noise, is simple and easy to implement, and has long calculation time and poor smoothing effect.
The time-frequency analysis denoising method comprises Gabor transformation, wavelet transformation, empirical mode decomposition and the like. Both the Gabor filtering method and the wavelet threshold denoising method filter interference noise by manually setting a threshold, have no self-adaptability and cannot acquire accurate phase information; the empirical mode decomposition method has the defects of mode aliasing, over-enveloping, under-enveloping and the like, and can not effectively process noise.
Disclosure of Invention
The invention provides a DSPI phase filtering method combining IVMD and energy estimation, which aims at a large amount of noise interference in a digital speckle phase diagram, adopts IVMD to process the speckle phase diagram, does not need parameter setting, can decompose the image, removes the noise interference and obtains accurate measurement information, and is described in detail as follows:
a DSPI phase filtering method combining IVMD and energy estimation, the DSPI phase filtering method comprising the steps of:
collecting a DSPI phase diagram, estimating the number of decomposed modal components according to the length and the width of the phase diagram, and extracting the optimal modal quantity by using an orthogonal index;
carrying out variation mode decomposition on the DSPI phase diagram according to the number of the decomposed components to obtain a series of mode function components, namely frequency modulation and amplitude modulation components;
calculating the energy of the frequency modulation and amplitude modulation components, and estimating the energy of the noise components by using the energy of the decomposed components;
and analyzing a curve change diagram of the energy logarithm value and the modal component number, extracting a noise-free component, and obtaining a filtered DSPI phase diagram.
The method comprises the following steps of estimating the number of decomposed modal components according to the length and the width of a phase diagram, and extracting the optimal modal quantity by using an orthogonal index:
judging the length and the width of the picture, and calculating the number of modal components by using the length or the width when the length and the width of the picture are equal;
when the two are not equal, firstly, the range of the modal quantity is estimated according to the scale, the orthogonality after decomposition is respectively calculated, and the corresponding component number when the orthogonality is minimum is selected, namely the optimal modal quantity.
Wherein, the orthogonality after the respective calculation and the corresponding component number when the orthogonality is minimum are selected, namely the optimal mode number specifically:
and extracting the optimal modal quantity by utilizing the orthogonality index to obtain a proper modal component.
Further, the step of performing variation modal decomposition on the DSPI phase diagram according to the number of the decomposed components to obtain a series of modal function components specifically includes:
first defining modal components as finite bandwidths with different center frequencies; constructing a constraint variational equation according to the minimum principle of the sum of bandwidths of different modes;
aiming at a constraint variational equation, introducing an augmented Lagrange function to convert the constraint variational equation into a non-constraint variational equation;
calculating a saddle point of the augmented Lagrange function by adopting a multiplicative operator alternating direction method, namely, an optimal solution of a constraint variational equation;
and continuously updating the center frequency and the modal component, stopping updating when the modal component meets the iteration stop condition, and outputting the decomposed modal component.
Further, the step of calculating the fm component energy and estimating the energy of the noise component using the energy of the decomposed component specifically includes:
calculating the energy of the frequency modulation and amplitude modulation component of the DSPI phase diagram after IVMD decomposition;
the energy of the noise component is estimated from the frequency and amplitude modulated energy.
The step of analyzing the curve variation graph of the energy logarithm value and the modal component number, extracting a noise-free component, and obtaining a filtered DSPI phase diagram specifically comprises the following steps:
respectively calculating energy logarithm values of modal components and energy logarithm values of noise components after decomposition of the DSPI phase diagram, and drawing variation conditions of the two energy logarithm values according to the modal quantity K;
the two energy log values are linearly decreased along with the increase of K, the energy log value estimated at a noise-free position is subjected to mutation along with the increase of K, and the frequency modulation and amplitude modulation component after the mutation is a noise-free component;
and reconstructing the noise-free component to obtain a reconstructed DSPI phase diagram, namely a noise-reduced DSPI phase diagram.
The technical scheme provided by the invention has the beneficial effects that:
1. aiming at the characteristic that the modal quantity needs to be set in the VMD decomposition process, the invention provides an improved variational modal decomposition method, which can select the optimal modal quantity in a variational frame and process a speckle phase diagram to obtain the inherent modal component of a DSPI phase diagram;
2. aiming at the fact that the collected speckle phase diagram contains noise components, the invention estimates the noise energy by utilizing the energy of the decomposed modal components, filters the noise components according to the variation curve chart of the energy logarithm value along with the modal quantity K, obtains the filtered DSPI phase diagram, improves the signal-to-noise ratio and further improves the accurate phase information;
3. the IVMD and noise energy estimation combined method can adaptively reduce the noise of the speckle interference phase picture, and avoids the complicated parameter setting of the traditional filtering method.
Drawings
FIG. 1 is a DSPI phase filtering method combining IVMD and energy estimation;
FIG. 2 is a schematic diagram of a digital speckle interferometry system;
FIG. 3 is a diagram of the collected DSPI phase including noise;
fig. 4 is a speckle phase map after IVMD based filtering.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below.
Example 1
A DSPI phase filtering method combining IVMD and energy estimation, referring to fig. 1, the DSPI phase filtering method includes the following steps:
101: collecting a DSPI phase diagram, estimating the number of decomposed modal components according to the length and the width of the phase diagram, and extracting the optimal modal quantity by using an orthogonal index;
102: carrying out variation mode decomposition on the DSPI phase diagram according to the number of the decomposed components to obtain a series of mode function components, namely frequency modulation and amplitude modulation components;
103: calculating the energy of the frequency modulation and amplitude modulation components, and estimating the energy of the noise components by using the energy of the decomposed components;
104: and analyzing a curve change diagram of the energy logarithm value and the modal component number, extracting a noise-free component, and obtaining a filtered DSPI phase diagram.
Wherein, the step 101 is specifically as follows:
judging the length and the width of the picture, and calculating the number of modal components by using the length or the width when the length and the width of the picture are equal;
when the two are not equal, firstly, the range of the modal quantity is estimated according to the scale, the orthogonality after decomposition is respectively calculated, and the corresponding component number when the orthogonality is minimum is selected, namely the optimal modal quantity.
Wherein, the step 102 specifically includes:
first defining modal components as finite bandwidths with different center frequencies; constructing a constraint variational equation according to the minimum principle of the sum of bandwidths of different modes;
aiming at a constraint variational equation, introducing an augmented Lagrange function to convert the constraint variational equation into a non-constraint variational equation;
calculating a saddle point of the augmented Lagrange function by adopting a multiplicative operator alternating direction method, namely, an optimal solution of a constraint variational equation;
and continuously updating the center frequency and the modal component, stopping updating when the modal component meets the iteration stop condition, and outputting the decomposed modal component.
Further, the step 103 specifically includes:
calculating the energy of the frequency modulation and amplitude modulation component of the DSPI phase diagram after IVMD decomposition;
the energy of the noise component is estimated from the frequency and amplitude modulated energy.
Further, the step 104 specifically includes:
respectively calculating energy logarithm values of modal components and energy logarithm values of noise components after decomposition of the DSPI phase diagram, and drawing variation conditions of the two energy logarithm values according to the modal quantity K;
the two energy log values are linearly decreased along with the increase of K, the energy log value estimated at a noise-free position is subjected to mutation along with the increase of K, and the frequency modulation and amplitude modulation component after the mutation is a noise-free component;
and reconstructing the noise-free component to obtain a reconstructed DSPI phase diagram, namely a noise-reduced DSPI phase diagram.
In summary, in the embodiment of the present invention, through the steps 101 to 104, the DSPI phase diagram can be decomposed on the premise of the phase diagram containing noise, the noise energy is estimated according to the energy of the decomposed components, the energy logarithm change curve diagram is analyzed, the noise components are removed, and accurate measurement information is obtained.
Example 2
The scheme in embodiment 1 is further described below with reference to specific calculation formulas, examples, and fig. 2 to 4, and is described in detail below:
201: a digital speckle interferometry system is built by combining the figure 2, and a CCD camera in the digital speckle interferometry system is used for collecting DSPI phase diagrams before and after deformation of a measured disc;
the detailed operation of the step is as follows:
a digital speckle interferometry system is constructed and comprises a CCD camera, an imaging lens, a laser and the like.
The light path of the measuring system is shown in fig. 2, laser emitted by a laser is divided into two beams by a spectroscope, one beam irradiates the surface of a measured object, the other beam is transmitted along an optical fiber through a coupling lens to be used as object light, diffuse reflected light of the measured object sequentially passes through an optical wave and an imaging lens to form speckle interference with the object light, a DSPI phase diagram is collected by a CCD camera, the material of a measured disc panel is a copper sheet, and the collected DSPI phase diagram is shown in fig. 3.
The embodiment of the invention does not limit the size of the copper sheet and sets the size according to the requirement in practical application.
202: the number of the modal components after decomposition is set according to the size of the digital speckle interference phase diagram, and the detailed operation of the step is as follows:
1) decomposing any one-dimensional signal, wherein the number of the modal components after decomposition is as follows:
D=log2L-1
wherein, L is the length of the one-dimensional signal, and D is the number of the modal components after decomposition.
2) Assuming that the size of the DSPI phase diagram is M multiplied by N, and the number of the components after VMD decomposition is K;
and extracting the optimal modal quantity by utilizing the orthogonality index to obtain a proper modal component.
203: carrying out variation mode decomposition on the digital speckle interference phase diagram by using the number K of modal components to obtain a series of modal function components, namely frequency modulation and amplitude modulation components, wherein the detailed operation of the step is as follows:
the VMD method determines the frequency center and the bandwidth of the decomposed component by iteratively searching the optimal solution of the variation model in a variation frame, thereby being capable of decomposing the digital speckle phase diagram in a self-adaptive manner.
1) InitializationAnd n ← 0, constructing a variation problem, and correspondingly constraining variation equations as follows:
wherein f (x) is a DSPI phase diagram, uk(x) Obtaining a 2D analytic signal u of a single-side frequency spectrum for a component after 2D signal decomposition, namely an intrinsic mode function, by using Hilbert transformAS,k(x) Its mathematical expression is as follows:
wherein, ω iskα as center frequencykIs a penalty parameter; x is a vector of the picture; k is the number after decomposition; u. ofkAs decomposed components; δ (d)<x,ωk>Is a dirac function; δ (d)<x,ωk,⊥>) Is omegakInverse Fourier transform under frequency band ⊥ inverse Fourier transform, pi<x,ωk>Are parameters.
2) Aiming at the problem of constrained variation, introducing an augmented Lagrange function to convert the problem of constrained variation into the problem of unconstrained variation, wherein the mathematical expression of the augmented Lagrange function is as follows:
wherein the penalty parameter is αkLagrange function multiplier is lambda, lambda (x) is a multiplier function, ▽ is a calculation norm;<.>is a convolution.
3) In order to solve the problem of the optimal solution, a multiplication operator alternating direction method is adopted to calculate the saddle point of the augmented Lagrange function, namely the optimal solution of the constraint variation equation. The alternate update obtains modal components and center frequency mathematical expressions as follows:
wherein i is a parameter and the value range is 1 to k.
4) Updating Lagrange function multiplier lambda.
Wherein the content of the first and second substances,a frequency domain function that is a multiplier; τ is a coefficient;is a function of the frequency domain of f (x),is composed ofIs measured.
5) If it is notAnd ending the circulation, and outputting the modal components, otherwise, continuing the circulation.
Wherein the content of the first and second substances,for the decomposed modal components, ε is an infinitesimally small positive number.
204: calculating the energy value of the component of the DSPI phase diagram after VMD decomposition, estimating the energy of the noise component according to the energy value, analyzing two curve change diagrams according to the relation between the energy logarithm and the decomposed quantity K, and extracting the noise-free component;
1) calculating the energy value of the decomposed component, wherein the modal component after decomposition is uk(i, j) having an energy value of:
wherein E iskFor the energy of the decomposed modal components, M and N are the length and width of the DSPI phase picture, respectively.
2) Estimating the energy of the noise component in the picture by using the energy of the decomposed modal component:
wherein E is1The energy value of the first frequency modulation and amplitude modulation component after IVMD decomposition is obtained, and the parameters gamma and rho are respectively 0.719 and 2.01 after repeated experiments.
3) Calculating the logarithm value of the decomposed component by using the energy and the noise energy of the decomposed component:
F=log2Ek
wherein, F is the logarithm value of the modal component of DSPI after VMD decomposition, and H is the logarithm value of the estimated noise component.
4) And analyzing the change curve graphs of the two energy logarithm values along with the K, extracting a noise-free modal component according to the K value corresponding to the mutation position of the two curves, and reconstructing the noise-free modal component to obtain a filtered DSPI phase diagram.
In summary, in the embodiment of the present invention, through the steps 201 to 204, on the premise that the DSPI phase diagram containing a large amount of noise does not need to set parameters, the IVMD is adopted to perform adaptive decomposition on the DSPI phase diagram, so as to reduce noise according to energy, improve the signal-to-noise ratio of the phase diagram, and reduce the phase error.
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. A DSPI phase filtering method combining IVMD and energy estimation is characterized by comprising the following steps:
collecting a DSPI phase diagram, estimating the number of decomposed modal components according to the length and the width of the phase diagram, and extracting the optimal modal quantity by using an orthogonal index;
carrying out variation modal decomposition on the DSPI phase diagram according to the optimal modal quantity to obtain a series of modal function components, namely frequency modulation and amplitude modulation components;
calculating the energy of the frequency modulation and amplitude modulation components, and estimating the energy of the noise components by using the energy of the decomposed components;
analyzing a curve variation graph of the energy logarithm value and the modal component number, extracting a noise-free component, and obtaining a filtered DSPI phase diagram;
the step of estimating the number of the decomposed modal components according to the length and the width of the phase diagram and extracting the optimal modal quantity by using the orthogonal index specifically comprises the following steps:
judging the length M and the width N of the picture, and calculating the number of modal components by using the length or the width when the length M and the width N are equal;
when the two are not equal, firstly, estimating the range of the modal quantity according to the scale, respectively calculating the orthogonality after decomposition, and selecting the corresponding component number when the orthogonality is minimum, namely the optimal modal quantity;
and extracting the optimal modal quantity by utilizing the orthogonality index to obtain a proper modal component.
2. The method according to claim 1, wherein the step of obtaining a series of modal function components by performing a variational modal decomposition on the DSPI phase diagram according to the number of decomposed components comprises:
first defining modal components as finite bandwidths with different center frequencies; constructing a constraint variational equation according to the minimum principle of the sum of bandwidths of different modes;
aiming at a constraint variational equation, introducing an augmented Lagrange function to convert the constraint variational equation into a non-constraint variational equation;
calculating a saddle point of the augmented Lagrange function by adopting a multiplicative operator alternating direction method, namely, an optimal solution of a constraint variational equation;
and continuously updating the center frequency and the modal component, stopping updating when the modal component meets the iteration stop condition, and outputting the decomposed modal component.
3. The combination of IVMD and energy estimation DSPI phase filtering method of claim 1, wherein said step of computing FM component energy and estimating noise component energy using decomposed component energy comprises:
calculating the energy of the frequency modulation and amplitude modulation component of the DSPI phase diagram after IVMD decomposition;
the energy of the noise component is estimated from the frequency and amplitude modulated energy.
4. The method of claim 1, wherein the step of analyzing a curve variation graph of log values of energy and number of modal components, extracting a noise-free component, and obtaining a filtered DSPI phase diagram specifically comprises:
respectively calculating energy logarithm values of modal components and energy logarithm values of noise components after decomposition of the DSPI phase diagram, and drawing variation conditions of the two energy logarithm values according to the modal quantity K;
the two energy log values are linearly decreased along with the increase of K, the energy log value estimated at a noise-free position is subjected to mutation along with the increase of K, and the frequency modulation and amplitude modulation component after the mutation is a noise-free component;
and reconstructing the noise-free component to obtain a reconstructed DSPI phase diagram, namely a noise-reduced DSPI phase diagram.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711009315.2A CN107907542B (en) | 2017-10-25 | 2017-10-25 | IVMD and energy estimation combined DSPI phase filtering method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711009315.2A CN107907542B (en) | 2017-10-25 | 2017-10-25 | IVMD and energy estimation combined DSPI phase filtering method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN107907542A CN107907542A (en) | 2018-04-13 |
CN107907542B true CN107907542B (en) | 2020-05-29 |
Family
ID=61840954
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201711009315.2A Active CN107907542B (en) | 2017-10-25 | 2017-10-25 | IVMD and energy estimation combined DSPI phase filtering method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN107907542B (en) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108875170B (en) * | 2018-06-05 | 2021-04-16 | 天津大学 | Noise source identification method based on improved variational modal decomposition |
CN113449912B (en) * | 2021-06-24 | 2022-03-18 | 东北电力大学 | Space load situation sensing method based on artificial intelligence technology |
Family Cites Families (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DD299931A7 (en) * | 1989-06-27 | 1992-05-14 | Adw Ddr Inst Mechanik | METHOD AND ARRANGEMENT FOR DETECTING THERMALLY INDUCED OWN VOLTAGES AND THE RESISTANCE TO MATERIALS OF MATERIALS |
CN102937668B (en) * | 2012-11-08 | 2015-04-08 | 电子科技大学 | Electric system low-frequency oscillation detection method |
CN103020907B (en) * | 2012-12-04 | 2015-08-26 | 上海交通大学 | Based on the DSPI striped filtering system of two-dimensional ensemble empirical mode decomposition |
CN103017665A (en) * | 2012-12-04 | 2013-04-03 | 上海交通大学 | Fast filter system of digital speckle pattern interferometric fringes |
CN105674065A (en) * | 2016-01-18 | 2016-06-15 | 南京信息职业技术学院 | Variational mode decomposition-based method for locating leakage point of pipeline by acoustic emission |
CN205561775U (en) * | 2016-04-26 | 2016-09-07 | 盐城工学院 | Three dimension word speckle interference synchronous measurement devices |
CN105716536B (en) * | 2016-04-26 | 2018-09-28 | 盐城工学院 | A kind of 3-dimensional digital speckle interference method for synchronously measuring and device |
CN105956388B (en) * | 2016-04-27 | 2018-11-13 | 南京理工大学 | Human body vital sign signal separating method based on VMD |
CN105758644A (en) * | 2016-05-16 | 2016-07-13 | 上海电力学院 | Rolling bearing fault diagnosis method based on variation mode decomposition and permutation entropy |
CN105976340A (en) * | 2016-05-20 | 2016-09-28 | 山东师范大学 | Improved spin filtering algorithm based on wavelet decomposition |
CN106198015B (en) * | 2016-06-29 | 2018-05-25 | 潍坊学院 | A kind of VMD of rolling bearing, spectrum kurtosis and smooth iteration envelope Analysis Method |
CN106859648A (en) * | 2016-12-21 | 2017-06-20 | 湖南华诺星空电子技术有限公司 | Multiple target human body respiration signal monitoring method and device based on non-contact detection |
CN106767489B (en) * | 2017-03-17 | 2019-01-11 | 合肥工业大学 | Small dynamic deformation measuring system and measurement method in digital speckle interference face |
CN107167087A (en) * | 2017-05-12 | 2017-09-15 | 天津大学 | A kind of deformation of body measuring method based on experience wavelet transformation |
CN107229795B (en) * | 2017-06-02 | 2019-07-19 | 东北大学 | A kind of milling parameter recognition methods based on variation mode decomposition and Energy-Entropy |
CN107274015A (en) * | 2017-06-12 | 2017-10-20 | 华北电力大学(保定) | A kind of method and system of prediction of wind speed |
-
2017
- 2017-10-25 CN CN201711009315.2A patent/CN107907542B/en active Active
Also Published As
Publication number | Publication date |
---|---|
CN107907542A (en) | 2018-04-13 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Shi et al. | A novel fractional wavelet transform and its applications | |
CN107563969A (en) | DSPI phase filtering methods based on variation mode decomposition | |
Uss et al. | Image informative maps for component-wise estimating parameters of signal-dependent noise | |
US20150312495A1 (en) | Wavelet denoising of fringe image | |
CN113160088B (en) | Speckle interference phase image filtering evaluation method based on Sobel operator and image entropy | |
US8189958B2 (en) | Method of fast image reconstruction | |
CN107907542B (en) | IVMD and energy estimation combined DSPI phase filtering method | |
Xu et al. | A denoising algorithm via wiener filtering in the shearlet domain | |
Liu et al. | Adaptive sparse coding on PCA dictionary for image denoising | |
Xiao et al. | A denoising scheme for DSPI phase based on improved variational mode decomposition | |
Farouj et al. | Hyperbolic Wavelet-Fisz denoising for a model arising in Ultrasound Imaging | |
CN113917490A (en) | Laser wind finding radar signal denoising method and device | |
US20160131767A1 (en) | Nonlinear processing for off-axis frequency reduction in demodulation of two dimensional fringe patterns | |
Zhou et al. | Nonlocal means filtering based speckle removal utilizing the maximum a posteriori estimation and the total variation image prior | |
Farge et al. | Extraction of coherent bursts from turbulent edge plasma in magnetic fusion devices using orthogonal wavelets | |
CN108053379B (en) | DSPI phase extraction method based on improved variational modal decomposition | |
Zhang et al. | A reverberation noise suppression method of sonar image based on shearlet transform | |
Gao | Image denoising by non-subsampled shearlet domain multivariate model and its method noise thresholding | |
Mun et al. | Propagated guided image filtering for edge-preserving smoothing | |
CN114545350A (en) | Space signal parameter estimation method based on synchronous compression operator | |
Aouinet et al. | Electrocardiogram denoised signal by discrete wavelet transform and continuous wavelet transform | |
Khan et al. | A filtering scheme for phase map of active tissue interface strain based on adaptive variational mode decomposition | |
CN114004833B (en) | Composite material terahertz imaging resolution enhancement method, device, equipment and medium | |
Feng et al. | Perceptual fusion of infrared and visible image through variational multiscale with guide filtering | |
CN117132715B (en) | Method and device for reconstructing time-of-flight image based on physical driving noise robustness |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |