CN113077429B - Speckle quality evaluation method based on adjacent sub-area correlation coefficient - Google Patents
Speckle quality evaluation method based on adjacent sub-area correlation coefficient Download PDFInfo
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Abstract
The invention discloses a speckle quality evaluation method based on correlation coefficients of adjacent sub-areas. The method is characterized in that: the method comprises the following steps: s1: generating a simulated speckle pattern by using computer software MATLAB; s2: normalizing the simulated speckle pattern to ensure that the gray value of the simulated speckle pattern is distributed between 0 and 255; s3: calculating correlation coefficients of all adjacent subintervals with equal size in the simulated speckle pattern; s4: and verifying the effectiveness of the correlation coefficient speckle quality evaluation method. In the quality evaluation process of the speckle pattern, the zero-mean normalized cross-correlation coefficient between adjacent sub-areas in the speckle pattern is used as a speckle quality evaluation parameter, so that the calculation complexity is reduced, and the robustness on light intensity change is realized; meanwhile, the parameters have the same quality judgment standard and fixed value range in the speckle patterns with different sizes, so that the quality comparison of a plurality of speckle images can be accurately carried out, and the quality evaluation of a single speckle pattern can be carried out.
Description
Technical Field
The invention relates to the field of surface optics and optical measurement, in particular to a digital speckle correlation method, a binocular speckle stereoscopic vision system and the like.
Background
When white light is irradiated to the surface of a rough object, speckles distributed randomly are formed, and when the object deforms, a speckle field changes. The digital speckle correlation method is a full-field deformation measurement method based on the principle, and can effectively obtain the deformation field of the surface of the measured object by calculating the correlation coefficient of subintervals in speckle images before and after the deformation of the object, wherein the correlation coefficient generally refers to a zero-mean normalized cross-correlation coefficient with good illumination adaptability. Since the digital speckle correlation method has the advantages of non-contact, full-field measurement and the like, the method is widely applied to various fields, such as blade repair, reverse modulation optical communication and the like.
The digital speckle correlation method needs high accuracy, which has high requirements on the quality of speckle patterns, the higher the quality of the speckle patterns is, the more accurate the matching of speckle sub-regions in the speckle patterns before and after deformation is, the smaller the corresponding mean error and standard deviation are, and the measurement of the mean error or standard deviation needs integer pixel search and sub-pixel search, so that the calculation cost is too expensive. Therefore, the quality evaluation of the speckle images adopts simpler and more effective speckle quality evaluation parameters.
The existing speckle quality evaluation parameters can be divided into local parameters and global parameters, and mainly comprise average gray gradient, information entropy, speckle average particle size and the like. Although these parameters can effectively evaluate the quality of speckle images, they lack fixed standards, and when sample speckle images with the same size are not used as comparison objects, the quality of the speckle images cannot be evaluated through these parameters, so these speckle quality evaluation parameters are mostly used for comparing a plurality of speckle images, not for evaluating a single speckle image.
Disclosure of Invention
In order to overcome the defects of the existing speckle quality evaluation method, the invention provides a speckle quality evaluation method based on the correlation coefficient of adjacent sub-regions.
The technical scheme adopted by the invention for solving the technical problem is as follows: the speckle quality evaluation method based on the correlation coefficient of adjacent sub-regions is characterized in that: the method comprises the following steps:
s1: generating a simulated speckle image;
s2: normalizing the simulated speckle images;
s3: calculating correlation coefficients of all adjacent subintervals with equal sizes in the simulated speckle pattern;
s4: and verifying the effectiveness of the correlation coefficient speckle quality evaluation method.
Further, the step S1 of generating a simulated speckle image specifically includes:
s11: respectively setting the number of the speckle grains and the size of the speckle grains;
s12: calculating the gray value of each pixel point in the simulated speckle image through an exponential formula;
s13: and generating a simulated speckle image according to the calculated gray values of all the positions.
Further, the step S2 of normalizing the analog speckle image includes:
s21: recording the maximum gray value and the minimum gray value in the simulated speckle image generated in the step S1;
s22: subtracting the minimum gray value from the gray value at all pixel points in the simulated speckle image generated in the step S1, and dividing the difference between the maximum gray value and the minimum gray value to make the gray value range at all pixel points in the simulated speckle image between 0 and 1;
s23: and multiplying the gray values of all pixel points in the processed simulated speckle image by 255 to make the gray values of all pixel points in the simulated speckle image within the gray level of 8-bit color, thereby completing the normalization processing of the simulated speckle image.
Further, the step S3 of calculating correlation coefficients of all equal-sized adjacent subintervals in the simulated speckle pattern specifically includes:
s31: setting the radius r value of the sub-area to obtain sub-area images with all radii r in the simulated speckle pattern subjected to the normalization processing in the step S2;
s32: calculating according to the value of the radius r of the subarea to obtain the total number N of pixel points contained in the subarea image;
s33: obtaining corresponding subarea regions and S according to the adjacent subarea images 1 、S 11 、S 2 、S 22 、S 12 And the area elements in each area subregion area sum are respectively: the gray value of each pixel point of the reference subarea image, the square of the gray value of each pixel point of the reference subarea image, the gray value of each pixel point of the right adjacent subarea image, the square of the gray value of each pixel point of the right adjacent subarea image, and the product of the gray value of each pixel point of the reference subarea image and the gray value of the right adjacent pixel point.
S34: according to the total number N of the adjacent subarea image pixel points, the subarea area and the subarea S 1 、S 11 、S 2 、S 22 、S 12 And calculating to obtain the correlation coefficient between all the left and right adjacent subarea images.
Further, the step S4 of verifying the effectiveness of the correlation coefficient speckle quality evaluation method includes the specific steps of:
s41: performing displacement operation in the X-axis direction on the speckles in the simulated speckle image generated in the step S1;
s42: randomly setting n measuring points in the simulated speckle image before the displacement operation, wherein the distance between the measuring points is 10 pixels;
s43: the integral pixel position of all the measuring points in the simulated speckle image after the displacement operation is solved through integral pixel search, and the integral pixel displacement of all the measuring points is calculated;
s44: obtaining the sub-pixel positions of all the measuring points in the simulated speckle image after the displacement operation through sub-pixel search, and calculating the sub-pixel displacement of all the measuring points;
s45: obtaining the horizontal displacement of all measuring points according to the whole pixel displacement and the sub-pixel displacement, and comparing the horizontal displacement with the real displacement of the displacement operation in the step S41 to obtain the mean error and the standard deviation of the horizontal displacement of the measuring points;
s46: calculating the average value of the correlation coefficients of all adjacent subareas in the step S3 as a speckle quality evaluation parameter, comparing the average value with the average value error and standard deviation of the horizontal displacement of the measuring point in the displacement experiment, and verifying the effectiveness of the speckle quality evaluation method based on the correlation coefficients of the adjacent subareas;
s47: the maximum value of the mean error of the horizontal displacement of the measuring point of the speckle image with excellent quality in the displacement experiment is set to be 0.01, and the fact that the zero-mean normalization coefficient of the average neighbor provided by the invention can evaluate the quality of a single speckle image with any size through a fixed standard value is verified.
Further, the simulated speckle images are generated by computer software MATLAB.
Further, the correlation coefficient is a zero-mean normalized cross-correlation coefficient.
Further, the integer pixel searching mode is the same-row searching.
Further, the sub-pixel searching method is a quadratic surface fitting method.
The invention has the following beneficial effects:
1. in the process of evaluating the speckle quality, the quality of the speckle image is evaluated by adopting the correlation coefficient of the adjacent subintervals, the quality evaluation is carried out on the speckle image by analyzing the shape size of speckles or the change amplitude of adjacent pixels and the gray distribution of the speckle image in the prior art, the calculation of the correlation coefficient of the adjacent subregions is simpler, the adopted correlation coefficient is a zero-mean normalized cross-correlation coefficient, the robustness is realized on the change of the light intensity, and the value change cannot occur due to the change of the brightness of the speckle image.
2. Compared with other speckle quality evaluation methods, the method can not only compare the quality of a plurality of speckle images, but also evaluate the quality of a single speckle image. The existing speckle quality evaluation parameters have no fixed value range, and when the size of a speckle image is changed, the value range is also changed, so that a fixed standard value cannot be set for evaluating the quality of a single speckle image with any size. The zero-mean normalized cross-correlation coefficient is used as an evaluation parameter, the value range of the zero-mean normalized cross-correlation coefficient is 0-1, the zero-mean normalized cross-correlation coefficient is irrelevant to the size of the speckle images, the speckle images of all sizes have the same quality standard, and the value of the zero-mean normalized cross-correlation coefficient is 0.9.
Drawings
Fig. 1 is a schematic flow chart of a speckle quality evaluation method based on correlation coefficients of adjacent sub-regions according to an embodiment of the present application.
Fig. 2(a) -2 (b) are graphs of horizontal displacement versus mean error and standard deviation in displacement measurement experiments for speckle images of different masses provided by embodiments of the present application.
Detailed Description
For a more clear description of the procedures and advantages of the embodiments of the present application, reference is now made to the following detailed description of the invention, which is illustrated in the accompanying drawings.
Referring to fig. 1, the speckle quality evaluation method based on the correlation coefficients of adjacent sub-regions specifically comprises the steps of,
s1: generating a simulated speckle image;
s2: normalizing the simulated speckle images;
s3: calculating correlation coefficients of all adjacent subintervals with equal size in the simulated speckle pattern;
s4: and verifying the effectiveness of the correlation coefficient speckle quality evaluation method.
The specific step of generating the simulated speckle image in step S1 includes:
s11: respectively setting the number of the speckle grains and the size of the speckle grains;
s12: calculating the gray value I (x, y) of a pixel point (x, y) in the simulated speckle image according to the number of the speckle particles and the size of the speckle particles, wherein the calculation formula is as follows:
wherein S represents the number of speckle particles, a represents the size of the speckle particles, I 0 Representing the peak gray value, x, of the speckle image k And y k Respectively, the center positions of the k-th speckle particles.
S13: and generating a simulated speckle image according to the calculated gray values of all the positions, wherein the generation software is MATLAB.
The step S2 of normalizing the analog speckle image includes the following steps:
s21: recording the maximum gray value and the minimum gray value in the simulated speckle image generated in the step S1, which are respectively denoted as I min And I max ；
S22: subtracting the minimum gray value from the gray value at all pixel points in the simulated speckle image generated in the step S1, and dividing the difference between the maximum gray value and the minimum gray value to make the gray value range at all pixel points in the simulated speckle image between 0 and 1;
s23: and multiplying the gray values of all pixel points in the processed simulated speckle image by 255 to make the gray values of all pixel points in the simulated speckle image within the gray level of 8-bit color, thereby completing the normalization processing of the simulated speckle image. The normalized simulated speckle image I is:
step S3, the specific step of calculating correlation coefficients of all equal-sized adjacent subintervals in the simulated speckle pattern includes:
s31: setting the value of the radius r of the sub-area to obtain sub-area images with all the radii r in the simulated speckle pattern after the normalization processing in the step S2;
s32: and calculating according to the value of the radius r of the subarea to obtain the total number of pixel points contained in the subarea image: n ═ 2r +1) 2 ；
S33: obtaining corresponding subarea regions and S according to the adjacent subarea images 1 、S 11 、S 2 、S 22 、S 12 And the area elements in each area subarea area sum are respectively: the gray value of each pixel point of the reference sub-area image, the square of the gray value of each pixel point of the reference sub-area image, the gray value of each pixel point of the right adjacent sub-area image, the square of the gray value of each pixel point of the right adjacent sub-area image, and the product of the gray value of each pixel point of the reference sub-area image and the gray value of the right adjacent pixel point;
subregion and S 1 、S 11 、S 2 、S 22 、S 12 The calculation formula of (a) is as follows:
s34: according to the total number N of the pixel points of the subarea image, the subarea area and the subarea S 1 、S 11 、S 2 、S 22 、S 12 And calculating to obtain the correlation coefficient among all the left and right adjacent subarea images.
The correlation coefficient is a zero-mean normalized cross-correlation coefficient, and the calculation formula is as follows:
referring to fig. 2, the step S4 of verifying the effectiveness of the correlation coefficient speckle quality evaluation method includes the specific steps of:
s41: performing displacement operation in the X-axis direction on the speckles in the simulated speckle image generated in the step S1;
the generation formula of the simulated speckle image after the displacement operation is as follows:
wherein u is 0 Representing the displacement in the direction of the x-axis, v 0 Indicating displacement in the y-axis direction.
S42: randomly setting n measuring points in the simulated speckle image before the displacement operation, wherein the distance between the measuring points is 10 pixels;
s43: the integral pixel position of all the measuring points in the simulated speckle image after the displacement operation is solved through integral pixel search, and the integral pixel displacement of all the measuring points is calculated;
s44: obtaining the sub-pixel positions of all the measuring points in the simulated speckle image after the displacement operation through sub-pixel search, and calculating the sub-pixel displacement of all the measuring points;
the sub-pixel searching method is a quadratic surface fitting method, and the expression is as follows:
C(x,y)＝a 0 +a 1 x+a 2 y+a 3 x 2 +a 4 xy+a 5 y 2 (10)
wherein C (x, y) represents the corresponding correlation coefficient value at the (x, y) pixel point, a 0 、a 1 、a 2 、a 3 、a 4 、a 5 Expressing the coefficient value to be solved, respectively substituting the coordinate values of the pixel point obtained in integer pixel search and the 8 pixel points around the pixel point and the corresponding correlation coefficient value, and solving the coefficient a by the least square method 0 、a 1 、a 2 、a 3 、a 4 、a 5 The sub-pixel displacement is calculated as:
s45: obtaining the horizontal displacement of all measuring points according to the whole pixel displacement and the sub-pixel displacement, and comparing the horizontal displacement with the real displacement of the displacement operation in the step S41 to obtain the mean error and the standard deviation of the horizontal displacement of the measuring points;
the horizontal displacement of the ith measuring point is as follows: u. of i ＝x i +Δx i ；
The expression of the mean error is: u. u c ＝∑u i /n-u 0 Wherein n represents the total number of the measuring points;
the expression of the standard deviation is as follows:
s46: calculating the average value of the correlation coefficients of all the adjacent subareas in the step S3 as the speckle quality evaluation parameter delta m Comparing with the mean error and standard deviation of the horizontal displacement of the measuring point in the displacement experimentVerifying the effectiveness of the speckle quality evaluation method based on the correlation coefficient of the adjacent sub-areas;
the speckle quality evaluation parameter δ m The expression of (a) is:
wherein W and H are the width and height of the speckle image, respectively, and C ij Represents (x) i ,y j ) A correlation coefficient value between a pixel point and a pixel point right adjacent to the point.
The speckle quality evaluation parameter δ m The comparison mode with the mean error and standard deviation of the horizontal displacement of the measuring point in the displacement experiment is as follows: for different delta m Corresponding speckle pattern, mean error u c Sum standard deviation σ c The graphs relating to the horizontal displacement are shown in FIG. 2(a) and FIG. 2(b), respectively, and δ m The larger the mean error u c And standard deviation σ c The smaller the amplitude of the curve between the horizontal displacement of the measuring point and the measuring point is, the better the quality of the simulated speckle image is, and the effectiveness of the speckle quality evaluation method based on the correlation coefficient of the adjacent sub-areas is proved.
S47: setting the maximum value of the mean error of the horizontal displacement of the measuring point of the speckle image with excellent quality in a displacement experiment to be less than 0.01pixel, and verifying that the zero-mean normalization coefficient of the average neighbor provided by the invention can carry out quality evaluation on a single speckle image with any size through a fixed standard value;
respectively setting different speckle image sizes, generating a simulated speckle image with the maximum value of the mean error of the horizontal displacement of the measuring point in the displacement experiment being 0.01pixel +/-0.001 pixel by the step S1, and calculating the zero-mean normalization coefficient delta of the average neighbor provided by the invention by the formula (14) m As shown in Table 1, delta is the amount of change in the speckle image size m The value is always stabilized at about 0.90, and the quality evaluation of a single speckle image can be verified.
Table 1: delta for different size standard quality speckle images m Value of
| Size | 100×100 | 300×300 | 500×500 | 700×700 |
| S/a | 2000/2.1210 | 1717/2.2090 | 1925/2.1910 | 2223/2.2530 |
| δ m | 0.8990 | 0.9019 | 0.9015 | 0.9071 |
The foregoing merely describes exemplary embodiments of implementations and it will be apparent to those skilled in the art that the present disclosure is not limited to the details set forth herein. Therefore, the technical scope of the present invention should be indicated by the appended claims rather than the above-described embodiments, and all changes which can be made in the details of the embodiments without changing the original meaning of the claims should be embraced within the scope of the present application.
Claims (6)
1. The speckle quality evaluation method based on the correlation coefficient of adjacent sub-regions is characterized in that: the speckle quality evaluation process comprises the following steps:
s1: generating a simulated speckle image;
s2: normalizing the analog speckle image;
s3: calculating correlation coefficients of all adjacent subintervals with equal sizes in the simulated speckle pattern, and the specific steps are as follows:
s31: setting the value of the radius r of the sub-area to obtain sub-area images with all the radii r in the simulated speckle pattern after the normalization processing in the step S2;
s32: calculating according to the value of the radius r of the subarea to obtain the total number N of pixel points contained in the subarea image;
s33: obtaining corresponding subarea regions and S1, S11, S2, S22 and S12 according to the adjacent subarea images, wherein the area elements in each region subarea region and the corresponding subarea regions are respectively as follows: the gray value of each pixel point of the reference subarea image, the square of the gray value of each pixel point of the reference subarea image, the gray value of each pixel point of the right adjacent subarea image, the square of the gray value of each pixel point of the right adjacent subarea image, and the product of the gray value of each pixel point of the reference subarea image and the gray value of the right adjacent pixel point;
s4: the effectiveness of the correlation coefficient speckle quality evaluation method is verified, and the method comprises the following specific steps:
s41: performing displacement operation in the X-axis direction on the speckles in the simulated speckle image generated in the step S1;
s42: randomly setting n measuring points in the simulated speckle image before the displacement operation, wherein the distance between the measuring points is 10 pixels;
s43: the integral pixel position of all the measuring points in the simulated speckle image after the displacement operation is solved through integral pixel search, and the integral pixel displacement of all the measuring points is calculated;
s44: obtaining the sub-pixel positions of all the measuring points in the simulated speckle image after the displacement operation through sub-pixel search, and calculating the sub-pixel displacement of all the measuring points;
s45: obtaining the horizontal displacement of all measuring points according to the whole pixel displacement and the sub-pixel displacement, and comparing the horizontal displacement with the real displacement of the displacement operation in the step S41 to obtain the mean error and the standard deviation of the horizontal displacement of the measuring points;
s46: and calculating the average value of the correlation coefficients of all the adjacent sub-areas in the step S3 as a speckle quality evaluation parameter, wherein the parameter is a zero-mean normalized cross-correlation coefficient. Comparing the average error and the standard deviation of the horizontal displacement of the measuring point in the displacement operation, and verifying the effectiveness of the speckle quality evaluation method based on the correlation coefficient of the adjacent sub-areas;
s47: the maximum value of the mean value error of the horizontal displacement of the measuring point in the displacement operation of the speckle image with excellent quality is set to be 0.01, and the quality evaluation of the single speckle image with any size can be carried out by verifying the zero-mean normalization coefficient of the average neighbor through a fixed standard value.
2. The speckle quality evaluation method based on the correlation coefficient of the adjacent sub-regions according to claim 1, characterized in that: the specific step of generating the simulated speckle image in step S1 includes:
s11: respectively setting the number of the speckle grains and the size of the speckle grains;
s12: calculating the gray value of each pixel point in the simulated speckle image through an exponential formula;
s13: and generating a simulated speckle image according to the calculated gray values of all the positions.
3. The speckle quality evaluation method based on the correlation coefficient of the adjacent sub-regions according to claim 1, characterized in that: the step S2 of normalizing the analog speckle image includes the following specific steps:
s21: recording the maximum gray value and the minimum gray value in the simulated speckle image generated in the step S1;
s22: subtracting the minimum gray value from the gray value at all pixel points in the simulated speckle image generated in the step S1, and dividing the difference between the maximum gray value and the minimum gray value to make the gray value range at all pixel points in the simulated speckle image between 0 and 1;
s23: and multiplying the gray values of all pixel points in the processed simulated speckle image by 255 to make the gray values of all pixel points in the simulated speckle image within the gray level of 8 bit colors, thereby completing the normalization processing of the simulated speckle image.
4. The speckle quality evaluation method based on the correlation coefficient of the adjacent sub-regions according to claim 2, characterized in that: the simulated speckle images are generated by computer software MATLAB.
5. The speckle quality evaluation method based on the correlation coefficient of the adjacent sub-regions according to claim 1, characterized in that: the whole pixel searching mode is the same-row searching.
6. The speckle quality evaluation method based on the correlation coefficient of the adjacent sub-regions according to claim 1, characterized in that: the sub-pixel searching method is a quadratic surface fitting method.
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