CN110246152A - PIV image processing method and system - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
- G01P5/001—Full-field flow measurement, e.g. determining flow velocity and direction in a whole region at the same time, flow visualisation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
- G01P5/18—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring the time taken to traverse a fixed distance
- G01P5/22—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring the time taken to traverse a fixed distance using auto-correlation or cross-correlation detection means
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/207—Analysis of motion for motion estimation over a hierarchy of resolutions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/269—Analysis of motion using gradient-based methods
Abstract
The present invention provides a kind of PIV image processing methods and system in technical field of image processing, include the following steps: S1, it estimates displacement field: original PIV image A, image B being pre-processed based on cross-correlation method, two-dimensional linear interpolation is carried out to the displacement field of the image A and image B of acquisition, the pixel of displacement vector and PIV experiment image in displacement field is corresponded, obtains estimating displacement field；S2, image reconstruction: being reconstructed image A using the displacement field estimated value obtained in step S1, obtains reconstructed image B '；S3 corrects displacement field: being analyzed based on optical flow method reconstructed image B ' and image B, obtains amendment displacement field；S4, iterative processing: iterative step S2 and step S3.The advantage of set cross-correlation method and optical flow method of the present invention has extraordinary treatment effect for the PIV experiment image of high dynamic range, solves the contradiction that the displacement computational accuracy of big displacement and sub-pixel cannot meet simultaneously.
Description
Technical field
The present invention relates to technical field of image processing, specifically, being related to a kind of PIV image processing method and system.
Background technique
Particle image velocimetry (PIV) is that a kind of common non-intrusion type quantifies velocity measuring technique, because its whole flow field, high-precision and
Fluid motion does not generate the characteristics of interference, is widely used in fluid velocity field measurement.
Precision, spatial resolution and the dynamic range for improving PIV image procossing help to improve PIV experiment flow analysis water
It is flat.PIV digital image processing method at this stage mainly has cross-correlation method (cross-correlation) and light stream
(optical-flow) two kinds.
Cross-correlation method is a kind of statistical method based on correlation convolution, and defect is as follows:
1, due to using discrete convolution function, this method can not obtain the displacement vector result of sub-pixel precision.Current
It needs to obtain sub-pixel precision by the way of related peak interpolation in experimental applications, the accuracy and reliability of difference result is not
When foot, the especially displacement of processing sub-pixel.
2, spatial resolution depends on interpretation area size.It improves resolution ratio and needs to reduce interpretation area size.When interpretation area mistake
Hour, it can be because the too low generation of signal-to-noise ratio calculates mistake, there are bottlenecks for resolution ratio.
3, when flow field velocity gradient is larger, interpretation grid can occur it is severely deformed cause correlation to reduce, cause interpretation
Area's matching error.
Optical flow method is that one kind is widely used in computer vision field, but is just attempted in research in recent years
Image processing method in PIV image procossing.Defect is as follows at this stage:
1, only when handling micro-displacement, precision is higher, can not handle the displacement of large span.
2, processing accuracy is affected by picture noise.
In current PIV experiment, the displacement field stream field details for needing to obtain higher resolution is analyzed, but is passed through
Current algorithm is also unable to reach this effect.
It is retrieved through the prior art, Chinese invention patent number is CN201910105156.9, entitled a kind of based on volume
The particle image velocimetry method of product neural network, it is extracted from two dimensional fluid particle picture using the method solution of supervised learning
The problem of velocity field.This method includes generating PIV data set, building neural network model, read particle picture, pretreatment, net
Network operation, post-processing step.Wherein, there are two types of modes for PIV data: first is that known speed field generates particle figure, second is that existing real
Test particle figure formation speed field.Network model is to obtain PIV convolutional neural networks by training parameter using convolutional neural networks
Model, input is two images, and output is the velocity vector field of each pixel on image.Its one kind utilized is based on correlation
The statistical method of property convolution, there is also one or more defects in above-mentioned.
Summary of the invention
For the defects in the prior art, the object of the present invention is to provide a kind of PIV image processing method and systems.
A kind of PIV image processing method provided according to the present invention, includes the following steps:
S1 estimates displacement field: being pre-processed based on cross-correlation method to original PIV image A, image B, to the figure of acquisition
As the displacement field progress two-dimensional linear interpolation of A and image B, make the pixel one of the displacement vector and PIV experiment image in displacement field
One is corresponding, obtains estimating displacement field；
S2, image reconstruction: being reconstructed image A using the displacement field estimated value obtained in step S1, obtains reconstruct image
As B '；
S3 corrects displacement field: being analyzed based on optical flow method reconstructed image B ' and image B, obtains amendment displacement field；
S4, iterative processing: iterative step S2 and step S3.
In some embodiments, the cross-correlation method in the step S1 uses distortion of the mesh cross-correlation method, passes through sub- picture
Plain interpolation obtains sub-pixel precision, and post-processes to result.
In some embodiments, the distortion of the mesh cross-correlation method the following steps are included:
S1-1 pre-processes original PIV image by the method for CLAH, high-pass filtering and high-intensitive limitation；
S1-2, the discrete cross-correlation function of standardization of application carry out the matching of interpretation area, take following form:
Wherein I is the corresponding PIV experiment image of image A, and I ' is the corresponding PIV experiment image of image B, and i, j are interpretation area
Center position coordinates, x, y are relative coordinate in interpretation area, Cii(x, y) is to standardize discrete cross-correlation related coefficient, μIFor image
A corresponds to average strength in sample area；μI′(x, y) is that image B corresponds to average strength in sample area；σI(x, y) is in image A
Corresponding sample area intensity distribution otherness, for cross-correlation coefficient CIIIt is standardized, it is also writeable to be σI, because it is not with seat
Mark changes；σI' (x, y) is to correspond to sample area intensity distribution otherness in image B, for cross-correlation coefficient CIIIt is marked
Standardization, M are that N is the coordinate in reconstructed image；
S1-3, using Gaussian peak the Fitting Calculation relevant peaks coordinate:
x0,y0For relevant peaks coordinate, the as motion vector in interpretation district center shop, C (i, j) is to standardize discrete cross-correlation
Distribution.
In some embodiments, the process of the post-processing passes through global displacement vector distribution screening method and part first
Intermediate value screening method screening error vector is standardized, the error vector for secondly going out screening is according to the weighted average knots of surrounding vectors
Fruit is replaced.
In some embodiments, reconstructed image B ' is obtained by the following method in the step S2:
Be for coordinate in A image (i, j) pixel displacement after intensity distribution matrix, m, n be reconstructed image in
Coordinate；I ', j ' are the coordinate that coordinate is after the pixel displacement of (i, j) in former experimental image, are calculated by the following method:
I '=i+u(i,j)
J '=j+v(i,j)
δ is calculated by the following method:
Obtain final reconstructed image:
Wherein A(i,j)For former experimental image, u and v are respectively the horizontal component and vertical component of motion vector, u(i,j)With
v(i,j)For the displacement field of estimating from A to B, Q(i,j)Be for coordinate in A image (i, j) pixel displacement after intensity distribution matrix.
In some embodiments, displacement field is modified by such as minor function in the step S3:
For each pixel, estimate that the quadratic polynomial indicated in local coordinate system is unfolded to carry out its approximate neighborhood:
F (x)~xTAx+bTx+c
Wherein, T be matrix transposition symbol, f (x) be certain point field in intensity distribution matrix, symmetrical matrix A, vector b and
Scalar c is that the signal value being fitted in the neighborhood by weighted least-squares obtains, and fitting obtains image B ' through the above way
Polynomial expansion A1(x),b1(x),c1(x) and the polynomial expansion A of image B2(x),b2(x),c2(x)；
It is averaged:
And it introduces:
Solve displacement d (x):
A (x) d (x)=Δ b (x)
Obtain displacement field u '(i,j),v′(i,j)；
The displacement field that combination interpolation displacement field and optical flow method obtain obtains final result:
U(i,j)=u(i,j)+u′(i,j)
V(i,j)=v(i,j)+v′(i,j)。
A kind of PIV image processing system, including estimate displacement field module, image reconstruction module, amendment displacement field module:
It is described estimate displacement field module and handled based on cross-correlation method and obtain the displacement field of PIV original image A, B estimate
Value；
Image A is reconstructed described image reconstructed module, obtains reconstructed image B '；
The amendment displacement field module analyzes reconstructed image B ' and image B, obtains amendment displacement field.
In some embodiments, the processing step for estimating displacement field module using cross-correlation method to PIV original image
Suddenly are as follows: original PIV image A and B are pre-processed by the method for CLAHE, high-pass filtering and high-intensitive limitation, respectively obtained
PIV experiment image I and I ', and then the discrete cross-correlation function of standardization of application carries out the matching of interpretation area, obtains interpretation district center point
After motion vector, the center point amount of shifting to is post-processed, swears the displacement in displacement field finally by two-dimensional linear interpolation
Amount and the pixel of PIV experiment image correspond, and obtain estimating displacement field.
In some embodiments, described image reconstructed module is based on displacement field estimated value and image A is reconstructed, and passes through
The mode of superposition obtains final reconstructed image B '.
In some embodiments, the amendment displacement field module application streamer method divides reconstructed image B ' and image B
Analysis, and the amendment displacement field result obtained using circulation is carried out image reconstruction as the initial displacement field estimation of next circulation and changed
For optimization processing.
Compared with prior art, the present invention have it is following the utility model has the advantages that
1, the advantage of present invention set cross-correlation method and optical flow method, has the PIV experiment image of high dynamic range
Extraordinary treatment effect solves the contradiction that the displacement computational accuracy of big displacement and sub-pixel cannot meet simultaneously.
2, the present invention can obtain super-resolution as a result, the density of displacement vector and pixel density phase i.e. in calculated result
Together, be conducive to the analysis of Flow details.
3, the present invention can inhibit influence of the picture noise to treatment effect to a certain extent, for imaging flaw
PIV experiment image processing result reliability it is preferable.
4, the present invention does not depend on highdensity grid to realize high-precision cross-correlation calculation, thus consume computing resource compared with
It is few, reduce the processing time of extensive PIV experiment image processing process.
Detailed description of the invention
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention,
Objects and advantages will become more apparent upon:
Fig. 1 is PIV image processing method flow diagram of the invention；
Fig. 2 is based on the exemplary flow chart of image processing program of the invention；
Fig. 3 is the PIV experiment picture displacement field estimation method process based on cross-correlation method applied in the present invention；
Fig. 4 is pixel displacement schematic diagram in image reconstruction step of the present invention；
Fig. 5 is that reconstructed image generates schematic diagram in image reconstruction step of the present invention；
Fig. 6 is based on image reconstruction iteration optimization PIV image processing method of the invention.
Specific embodiment
The present invention is described in detail combined with specific embodiments below.Following embodiment will be helpful to the technology of this field
Personnel further understand the present invention, but the invention is not limited in any way.It should be pointed out that the ordinary skill of this field
For personnel, without departing from the inventive concept of the premise, several changes and improvements can also be made.These belong to the present invention
Protection scope.
Embodiment 1:
As shown in figures 1 to 6, the present patent application provides a kind of PIV Super-resolution analysis based on image reconstruction hybrid algorithm
Method, as shown in Fig. 2, main includes estimating displacement field, image reconstruction, amendment displacement field, iterative processing.
It is illustrated in figure 2 the displacement field estimation implementation example based on cross correlation algorithm, mainly includes displacement field estimation, figure
As reconstruct, optical flow method computing module.The method pair limited by CLAHE (adaptive equalization), high-pass filtering and maximum intensity
Original PIV image A and B is pre-processed, and picture I, I ' are obtained.
On this basis, the discrete cross-correlation function of standardization of application carries out the matching of interpretation area.Defining I (i, j) is in picture
Coordinate is the light intensity of the pixel at (i, j), and it is respectively μ that the light intensity average value in Liang Ge interpretation area, which is respectively distribution function,I, μI′。
Calculate the correlation convolution in two picture interpretation areas
Calculate intensity distribution function
The related coefficient of normalized
By Cii(x, y) forms correlation plane C, passes through Gaussian peak the Fitting Calculation relevant peaks coordinate (x0,y0)
Relevant peaks coordinate is the motion vector of interpretation district center point.
The result is post-processed.Last handling process is divided into two steps:
Screening method and local standard intermediate value screening method screening error vector are distributed by global displacement vector.
The error vector that screening goes out is replaced according to the result of weighted average of surrounding vectors.
Two-dimensional linear interpolation is carried out to above-mentioned displacement field, makes the pixel of the displacement vector and PIV experiment image in displacement field
It corresponds, obtains displacement field u(i,j), v(i,j)。
It is illustrated in figure 3 the algorithm example of image reconstruction, which is designed for PIV image processing algorithm,
Have the characteristics that keep that light intensity is constant, sub-pixel precision.Lower array function is taken, weight is carried out to image by the estimated value of displacement field
Structure:
Wherein A(i,j)For former experimental image.
I ', j ' are the coordinate that coordinate is after the pixel displacement of (i, j) in former experimental image, are calculated by the following method:
I '=i+u(i,j)
J '=j+v(i,j)
δ is calculated by the following method:
Final reconstructed image is obtained according to stacked system as shown in Figure 4:
As shown in figure 5, analyzing by optical flow method reconstructed image B ' and image B, amendment displacement field, reaction weight are obtained
Difference after structure in image between particle position and practical particle position.
For each pixel, estimate that the quadratic polynomial indicated in local coordinate system is unfolded to carry out its approximate neighborhood:
F (x)~xTAx+bTx+c
Wherein, symmetrical matrix A, vector b and scalar c are that the signal value being fitted in the neighborhood by weighted least-squares obtains
's.Fitting obtains the polynomial expansion A of image B ' by this way1(x),b1(x),c1(x) and the polynomial expansion of image B
A2(x),b2(x),c2(x)。
It is averaged:
And it introduces:
Solve displacement d (x):
A (x) d (x)=Δ b (x)
Obtain displacement field u '(i,j),v′(i,j)。
The displacement field that combination interpolation displacement field and optical flow method obtain obtains final result:
U(i,j)=u(i,j)+u′(i,j)
V(i,j)=v(i,j)+v′(i,j)
The step is equivalent to the rougher interpolation displacement field of the modified result using optical flow method, obtains high-resolution, height
Precision, the result of high dynamic range.
It is illustrated in figure 6 a kind of based on image reconstruction iteration optimization PIV image processing method of the invention, is adopted in this method
The initial displacement field of the displacement field result for using circulation to obtain as next circulation is estimated.The process can optimize an existing
Displacement field calculated result, each iterative process all pass through the displacement field that optical flow method has modified estimation, can finally obtain high precision
The displacement field result of degree.
Embodiment 2:
As shown in figures 1 to 6, the present patent application provides a kind of PIV image processing system, including estimates displacement field module, image
Reconstructed module, amendment displacement field module:
It is described estimate displacement field module and handled based on cross-correlation method and obtain the displacement field of PIV original image A, B estimate
Value；
Image A is reconstructed described image reconstructed module, obtains reconstructed image B '；
The amendment displacement field module analyzes reconstructed image B ' and image B, obtains amendment displacement field.
It is described to estimate displacement field module using cross-correlation method to the processing step of PIV original image are as follows: by CLAHE,
The method of high-pass filtering and maximum intensity limitation pre-processes original PIV image A and B, respectively obtains PIV experiment image I
With I ', and then the discrete cross-correlation function of standardization of application carries out the matching of interpretation area, right after obtaining interpretation district center point motion vector
Central point motion vector is post-processed, and displacement vector and PIV experiment figure in displacement field are made finally by two-dimensional linear interpolation
The pixel of picture corresponds, and obtains estimating displacement field.
Described image reconstructed module is based on displacement field estimated value and image A is reconstructed, wherein being obtained by way of superposition
To final reconstructed image B '.
The amendment displacement field module application streamer method analyzes reconstructed image B ' and image B, and uses and recycle
The amendment displacement field result arrived carries out the processing of image reconstruction iteration optimization as the initial displacement field estimation of next circulation.
Correlation method and solution process involved in module operation in the present embodiment 2, have specific in above-described embodiment 1
It illustrates, no longer correlation method and solution process is repeated in the present embodiment 2 thus.
One skilled in the art will appreciate that in addition to realizing system provided by the invention in a manner of pure computer readable program code
It, completely can be by the way that method and step be carried out programming in logic come so that provided by the invention other than system, device and its modules
System, device and its modules are declined with logic gate, switch, specific integrated circuit, programmable logic controller (PLC) and insertion
The form of controller etc. realizes identical program.So system provided by the invention, device and its modules may be considered that
It is a kind of hardware component, and the knot that the module for realizing various programs for including in it can also be considered as in hardware component
Structure；It can also will be considered as realizing the module of various functions either the software program of implementation method can be Hardware Subdivision again
Structure in part.
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited to above-mentioned
Particular implementation, those skilled in the art can make a variety of changes or modify within the scope of the claims, this not shadow
Ring substantive content of the invention.In the absence of conflict, the feature in embodiments herein and embodiment can any phase
Mutually combination.
Claims (10)
1. a kind of PIV image processing method, which comprises the steps of:
S1 estimates displacement field: being pre-processed based on cross-correlation method to original PIV image A, image B, to the image A of acquisition
Two-dimensional linear interpolation is carried out with the displacement field of image B, keeps the pixel one of the displacement vector and PIV experiment image in displacement field a pair of
It answers, obtains estimating displacement field；
S2, image reconstruction: being reconstructed image A using the displacement field estimated value obtained in step S1, obtains reconstructed image B '；
S3 corrects displacement field: being analyzed based on optical flow method reconstructed image B ' and image B, obtains amendment displacement field；
S4, iterative processing: iterative step S2 and step S3.
2. PIV image processing method according to claim 1, which is characterized in that the cross-correlation method in the step S1 is adopted
With distortion of the mesh cross-correlation method, sub-pixel precision is obtained by sub-pixel interpolation, and result is post-processed.
3. PIV image processing method according to claim 2, which is characterized in that the distortion of the mesh cross-correlation method packet
Include following steps:
S1-1 pre-processes original PIV image by the method for CLAH, high-pass filtering and high-intensitive limitation；
S1-2, the discrete cross-correlation function of standardization of application carry out the matching of interpretation area, take following form:
Wherein I is the corresponding PIV experiment image of image A, and I ' is the corresponding PIV experiment image of image B, and i, j are interpretation district center
Position coordinates, x, y are relative coordinate in interpretation area, Cii(x, y) is to standardize discrete cross-correlation related coefficient, μIIt is A pairs of image
Answer average strength in sample area；μI′(x, y) is that image B corresponds to average strength in sample area；σI(x, y) is right in image A
Sample area intensity distribution otherness is answered, for cross-correlation coefficient CIIIt is standardized, it is also writeable to be σI, because it is not with coordinate
It changes；σI' (x, y) is to correspond to sample area intensity distribution otherness in image B, for cross-correlation coefficient CIICarry out standard
Change, M is that N is the coordinate in reconstructed image；
S1-3, using Gaussian peak the Fitting Calculation relevant peaks coordinate:
x0,y0For relevant peaks coordinate, the as motion vector in interpretation district center shop, C (i, j) is to standardize discrete cross-correlation distribution.
4. PIV image processing method according to claim 2, which is characterized in that the process of the post-processing passes through first
Global displacement vector is distributed screening method and local standard intermediate value screening method screening error vector, the mistake for secondly going out screening
Accidentally vector is replaced according to the result of weighted average of surrounding vectors.
5. PIV image processing method according to claim 1, which is characterized in that in the step S2 by the following method
Obtain reconstructed image B ':
Be for coordinate in A image (i, j) pixel displacement after intensity distribution matrix, m, n be reconstructed image in seat
Mark；I ', j ' are the coordinate that coordinate is after the pixel displacement of (i, j) in former experimental image, are calculated by the following method:
I '=i+u(i,j)
J '=j+v(i,j)
δ is calculated by the following method:
Obtain final reconstructed image:
Wherein A(i,j)For former experimental image, u and v are respectively the horizontal component and vertical component of motion vector, u(i,j)With v(i,j)For
Displacement field, Q are estimated from A to B(i,j)Be for coordinate in A image (i, j) pixel displacement after intensity distribution matrix.
6. PIV image processing method according to claim 1, which is characterized in that by such as minor function in the step S3
It is modified displacement field:
For each pixel, estimate that the quadratic polynomial indicated in local coordinate system is unfolded to carry out its approximate neighborhood:
F (x)~xTAx+bTx+c
Wherein, T is matrix transposition symbol, and f (x) is the intensity distribution matrix in certain point field, symmetrical matrix A, vector b and scalar
C is that the signal value being fitted in the neighborhood by weighted least-squares obtains, and fitting obtains the more of image B ' through the above way
A is unfolded in item formula1(x),b1(x),c1(x) and the polynomial expansion A of image B2(x),b2(x),c2(x)；
It is averaged:
And it introduces:
Solve displacement d (x):
A (x) d (x)=Δ b (x)
Obtain displacement field u '(i,j),v′(i,j)；
The displacement field that combination interpolation displacement field and optical flow method obtain obtains final result:
U(i,j)=u(i,j)+u′(i,j)
V(i,j)=v(i,j)+v′(i,j)。
7. a kind of PIV image processing system, which is characterized in that including estimating displacement field module, image reconstruction module, amendment displacement
Field module:
The displacement field discreet value for estimating displacement field module and being handled based on cross-correlation method and obtaining PIV original image A, B；
Image A is reconstructed described image reconstructed module, obtains reconstructed image B '；
The amendment displacement field module analyzes reconstructed image B ' and image B, obtains amendment displacement field.
8. PIV image processing system according to claim 7, which is characterized in that the displacement field module of estimating is using mutually
Processing step of the correlation technique to PIV original image are as follows: by the method for CLAHE, high-pass filtering and high-intensitive limitation to original
PIV image A and B is pre-processed, and respectively obtains PIV experiment image I and I ', so the discrete cross-correlation function of standardization of application into
The matching of row interpretation area post-processes the center point amount of shifting to, after obtaining interpretation district center point motion vector finally by two
Dimensional linear interpolation corresponds the pixel of displacement vector and PIV experiment image in displacement field, obtains estimating displacement field.
9. PIV image processing system according to claim 7, which is characterized in that described image reconstructed module is based on displacement
Image A is reconstructed in field estimated value, and final reconstructed image B ' is obtained by way of superposition.
10. PIV image processing system according to claim 7, which is characterized in that the amendment displacement field module application stream
Light method analyzes reconstructed image B ' and image B, and the amendment displacement field result obtained using circulation is as next circulation
Initial displacement field estimation carry out the processing of image reconstruction iteration optimization.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111473944A (en) * | 2020-03-18 | 2020-07-31 | 中国人民解放军国防科技大学 | PIV data correction method and device for observing complex wall surface in flow field |
| CN111767679A (en) * | 2020-07-14 | 2020-10-13 | 中国科学院计算机网络信息中心 | Method and device for processing time-varying vector field data |
| CN112697657A (en) * | 2021-03-24 | 2021-04-23 | 杭州电子科技大学 | Intelligent anemometry system based on aerosol particle images |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103605637A (en) * | 2013-11-28 | 2014-02-26 | 华中科技大学 | Particle image velocimetry vector estimation method for spatial resolution self-adaptation adjustment |
| CN106683114A (en) * | 2016-12-16 | 2017-05-17 | 河海大学 | Fluid motion vector estimation method based on feature optical flow |
| US20180253854A1 (en) * | 2014-11-14 | 2018-09-06 | The Regents Of The University Of California | Ultrasound-based volumetric particle tracking method |
| CN109669049A (en) * | 2019-02-01 | 2019-04-23 | 浙江大学 | A kind of particle image velocimetry method based on convolutional neural networks |
-
2019
- 2019-05-21 CN CN201910424730.7A patent/CN110246152B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103605637A (en) * | 2013-11-28 | 2014-02-26 | 华中科技大学 | Particle image velocimetry vector estimation method for spatial resolution self-adaptation adjustment |
| US20180253854A1 (en) * | 2014-11-14 | 2018-09-06 | The Regents Of The University Of California | Ultrasound-based volumetric particle tracking method |
| CN106683114A (en) * | 2016-12-16 | 2017-05-17 | 河海大学 | Fluid motion vector estimation method based on feature optical flow |
| CN109669049A (en) * | 2019-02-01 | 2019-04-23 | 浙江大学 | A kind of particle image velocimetry method based on convolutional neural networks |
Non-Patent Citations (2)
| Title |
|---|
| ZIFENG YANG ET AL.: "Hybrid particle image velocimetry with the combination of cross-correlation and optical flow method", 《JOURNAL OF VISUALIZATION》 * |
| 钟强龙: "基于光流的高超声速流场PIV算法研究及应用", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111473944A (en) * | 2020-03-18 | 2020-07-31 | 中国人民解放军国防科技大学 | PIV data correction method and device for observing complex wall surface in flow field |
| CN111473944B (en) * | 2020-03-18 | 2022-06-14 | 中国人民解放军国防科技大学 | PIV data correction method and device for observing complex wall surface in flow field |
| CN111767679A (en) * | 2020-07-14 | 2020-10-13 | 中国科学院计算机网络信息中心 | Method and device for processing time-varying vector field data |
| CN111767679B (en) * | 2020-07-14 | 2023-11-07 | 中国科学院计算机网络信息中心 | Method and device for processing time-varying vector field data |
| CN112697657A (en) * | 2021-03-24 | 2021-04-23 | 杭州电子科技大学 | Intelligent anemometry system based on aerosol particle images |
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