A method for image registration in wind tunnel experiments of temperature-sensitive paint

Abstract

The present invention provides an image registration method for a temperature-sensitive paint wind tunnel experiment, which comprises the following steps: 1) obtaining an image shot in a temperature-sensitive paint experiment, and determining a reference image f(x, y) and an image g(x, y) to be processed; 2) using an edge detection Candy algorithm to determine the edge of an experimental model and perform binarization processing, and then performing a morphological dilation operation to respectively determine mask images m1(x, y) and m2(x, y) of the reference image and the image to be processed; 3) rotating the image g(x, y) to be processed and its mask image m2(x, y) clockwise. Rotate 180 degrees to obtain new images g′(x,y) and m′2(x,y); 4) Perform Fourier transform on images f(x,y), g′(x,y), m1(x,y), and m′2(x,y) to obtain F(u,v), G′(u,v), M1(u,v), and M′2(u,v); 5) Define the normalized cross-correlation function ψ between the reference image and the image to be processed and determine the offset between the position where it reaches its maximum value and the center point of the ψ matrix. This offset is the offset between f(x,y) and g(x,y). This invention is well-conceived and utilizes the contour features of the experimental model to achieve sub-pixel precision registration of temperature-sensitive paint images, eliminating the need for marker points and fully leveraging the advantages of temperature-sensitive paint technology for surface measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of hypersonic aircraft measurement, and in particular to an image registration method for a temperature-sensitive paint wind tunnel experiment. Background Art

[0002] Heat flux measurements are fundamental to the design of hypersonic vehicle thermal protection systems, and related measurement techniques are a key focus of hypersonic research. Single-point heat flux measurement techniques, such as thermocouples and thermopiles, have played a crucial role in hypersonic vehicle thermal environment testing. However, due to practical considerations such as model size, strength, and sensor wiring, single-point heat flux sensors cannot be deployed densely in large numbers. Measurements from a few points also fail to accurately characterize the heat flux distribution. This is particularly true for areas with large heat flux gradients and complex distributions. Single-point heat flux measurement techniques are no longer fully capable of meeting the evolving demands of aircraft design. Surface heat flux measurement techniques are needed to capture detailed, full-field heat flux distributions. Temperature-sensitive paint is a rapidly developing surface heat flux measurement technique. This technique utilizes the thermal quenching effect of photoluminescence, whose radiation intensity decreases with increasing temperature. Temperature changes are determined by measuring the radiation intensity of the paint, and the heat flux is then determined by combining the material's physical properties. The data processing method for temperature-sensitive paint wind tunnel experiments is a key factor influencing the accuracy of temperature-sensitive paint measurement results. Due to the stiffness and fit accuracy of the model support system in wind tunnel experiments, the model vibrates during the experiment, causing slight displacements between images taken at different times. Failure to perform image registration during data processing will result in measurement errors. A commonly used image registration method is the marker point method, in which several markers are made on the model. Image registration uses the marker point cross-correlation method to calculate the marker point displacements and perform image registration. This method is computationally intensive and time-consuming, and because the marker points obscure the temperature-sensitive paint coating, using this method can result in missing information in the measurement results. This is especially true for areas with large heat flux gradients, where this method limits the advantages of measuring temperature-sensitive paint surfaces.

[0003] In summary, it is necessary to make further innovations to the existing technologies. Summary of the Invention

[0004] In response to the problems existing in the above-mentioned background technology, the present invention proposes a temperature-sensitive paint wind tunnel experimental image registration method with reasonable conception, which utilizes the contour characteristics of the experimental model itself to achieve sub-pixel precision registration of the temperature-sensitive paint image, avoids the need to make identification points, and gives full play to the technical surface measurement advantages of temperature-sensitive paint.

[0005] To solve the above technical problems, the present invention provides a temperature-sensitive paint wind tunnel test image registration method, which includes the following steps:

[0006] 1) Obtain images captured during the temperature-sensitive paint experiment and determine the reference image f(x, y) and the image g(x, y) to be processed;

[0007] 2) Use the Candy edge detection algorithm to determine the edge of the experimental model and perform binarization processing, and then perform morphological dilation operation to determine the mask images m1(x, y) and m2(x, y) of the reference image and the image to be processed respectively;

[0008] 3) Rotate the image to be processed g(x, y) and its mask image m2(x, y) 180 degrees clockwise to obtain new images g′(x, y) and m′2(x, y);

[0009] 4) After Fourier transforming the images f(x,y), g′(x,y), m1(x,y), and m′2(x,y), the Fourier transform results are F(u,v), G′(u,v), M1(u,v), and M′2(u,v), respectively;

[0010] 5) Define the normalized cross-correlation function ψ between the reference image and the image to be processed and determine the offset between the position where it takes the maximum value and the center point of the ψ matrix. This offset is the offset between f(x,y) and g(x,y).

[0011] The temperature-sensitive paint wind tunnel test image registration method, wherein: the mask images m1(x, y) and m2(x, y) in step 2) can be detected using the edge detection Candy algorithm to obtain the edges of the experimental model and then determined by morphological dilation operation; and the mask images m1(x, y) and m2(x, y) are binary images, the areas involved in the calculation are assigned a value of 1, and other areas are assigned a value of 0.

[0012] The temperature-sensitive paint wind tunnel test image registration method, wherein the specific process of step 4) is:

[0013] definition represents Fourier transform, and the calculation process of Fourier transform and inverse transform is shown in the following equations (1)-(2);

[0014]

[0015]

[0016] In formulas (1) and (2), u and v are frequency domain coordinates, M and N are the number of pixels in the x and y directions of the image, and i is the imaginary unit;

[0017] The Fourier transform results corresponding to the images f(x,y), g′(x,y), m1(x,y), and m′2(x,y) are F(u,v), G′(u,v), M1(u,v), and M′2(u,v), respectively.

[0018] The image registration method for the temperature-sensitive paint wind tunnel experiment, wherein the process of defining the normalized cross-correlation function of the reference image and the image to be processed in step 5) is:

[0019] First define represents the inverse Fourier transform, and then the temporary variables are calculated by the following formulas (4)-(6)

[0020]

[0021]

[0022]

[0023] In the above formulas (4)-(6), · represents the multiplication of the corresponding elements of the image matrix, Represents the new matrix obtained by multiplying the corresponding elements of matrix F and matrix G, and performing inverse Fourier transform on this new matrix. Represents the new matrix obtained by multiplying the corresponding elements of the matrix F and the matrix M′2, and performing the inverse Fourier transform on this new matrix; Represents the new matrix obtained by multiplying the corresponding elements of the matrix M1 and the matrix G', and performing the inverse Fourier transform on this new matrix; Represents the new matrix obtained by multiplying the corresponding elements of the matrices M1 and M2', and performing the inverse Fourier transform on this new matrix; It represents the Fourier transform of the corresponding elements of the matrix f and the matrix f, and then the new matrix obtained by multiplying it with the matrix M′2, and the inverse Fourier transform of this new matrix; It means that the matrix g' is multiplied by the corresponding elements of the matrix g', and then the Fourier transform is performed, and the new matrix is ​​multiplied by the matrix M1, and the inverse Fourier transform is performed on this new matrix;

[0024] Finally, the normalized cross-correlation function ψ of the reference image and the image to be processed is defined as:

[0025]

[0026] The normalized cross-correlation function ψ calculated by equation (3) is a matrix of (2M-1, 2N-1) with a value range of [-1, 1], where 1 indicates complete correlation and -1 indicates complete non-correlation. Find the position where ψ takes the maximum value, and its offset from the center point of the matrix is ​​the offset between the two images f(x, y) and g(x, y).

[0027] The temperature-sensitive paint wind tunnel experiment image registration method, wherein: in the step 1), a high-speed camera of a temperature-sensitive paint measurement system is used to capture and obtain temperature-sensitive paint experiment images.

[0028] By adopting the above technical solution, the present invention has the following beneficial effects:

[0029] The temperature-sensitive paint experimental image registration method of the present invention is rationally conceived and utilizes the contour features of the experimental model itself to achieve sub-pixel precision registration of the temperature-sensitive paint image, which can avoid the need to make marking points and give full play to the advantages of temperature-sensitive paint technical surface measurement.

[0030] The advantages of the temperature-sensitive paint experimental image registration method of the present invention over the marking point method are mainly reflected in the following aspects:

[0031] 1) The production of marking points will inevitably block the temperature-sensitive paint layer, making it impossible to obtain measurement results at the marking point position, limiting the advantage of high spatial resolution of temperature-sensitive paint measurement technology;

[0032] 2) In the identification point method, the number of pixels of the identification points in the image is much smaller than the number of pixels occupied by the model contour area; it can be seen that the image registration method based on the model's own contour features adopted in the present invention is much more accurate than the identification point method, thereby improving the accuracy of the temperature-sensitive paint measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 This is a diagram of the experimental model involved in the image registration method for the temperature-sensitive paint wind tunnel experiment of the present invention;

[0035] Figure 2 The original temperature-sensitive paint image involved in the temperature-sensitive paint wind tunnel experiment image registration method of the present invention;

[0036] Figure 3 This is a model edge detection result diagram involved in the image registration method for the temperature-sensitive paint wind tunnel experiment of the present invention;

[0037] Figure 4 The mask image involved in the image registration method for the temperature-sensitive paint wind tunnel experiment of the present invention;

[0038] Figure 5 The heat flux density distribution map of the model surface obtained after registration in the temperature-sensitive paint wind tunnel experiment image registration method of the present invention;

[0039] Figure 6 This is the heat flux density distribution map of the model surface obtained without registration in the image registration method of the temperature-sensitive paint wind tunnel experiment of the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0041] The present invention will be further explained below with reference to specific embodiments.

[0042] Thermosensitive paint measurement technology utilizes the thermal quenching effect of photoluminescence, whose radiation intensity decreases with increasing temperature. To eliminate the effects of factors such as non-uniform illumination, imaging viewing angle, and uneven thickness of the thermosensitive paint coating, the temperature change is determined by measuring the relative change in the radiation intensity of the thermosensitive paint compared to the radiation intensity at a reference temperature. In this experiment, the image before the flow field is established is generally used as the reference image, and the room temperature is used as the reference temperature. According to the principle of thermosensitive paint measurement, the grayscale values ​​of the images acquired during the experiment will change due to changes in the surface temperature of the experimental model. Since normalized cross-correlation is insensitive to the multiplication factor between the two images, a normalized cross-correlation function is used to describe the relative displacement between the two images. Calculating the normalized cross-correlation function in the spatial domain requires repeated calculations in the overlapping region of the two images. Calculating the function in the frequency domain using Fourier transforms can avoid this process and reduce the computational complexity. Therefore, a Fourier transform-based cross-correlation function is used for calculation. In addition, the captured image will contain noise in the entire area of ​​the image due to various reasons, while the contour of the model only exists in a small area of ​​the image. In order to minimize the influence of noise, the contour position of the model is first determined during the calculation process, and the calculation domain is limited to the area near the model contour through the mask image. Suppose the image of the temperature-sensitive paint taken before the experimental flow field is established is f(x,y), and the image of the temperature-sensitive paint taken at a certain moment during the experimental time is g(x,y). x,y are spatial coordinates, m1(x,y) and m2(x,y) are the mask images of f(x,y) and g(x,y), respectively, and the image sizes are the same as f(x,y) and g(x,y), respectively. m1(x,y) and m2(x,y) are binary images, the area involved in the calculation is assigned a value of 1, and the other areas are assigned a value of 0. The images of g(x,y) and m2(x,y) after being rotated 180 degrees clockwise are g′(x,y) and m2′(x,y). Definition and They represent Fourier transform and inverse Fourier transform respectively, and their calculation process is shown in equations (1)-(2).

[0043]

[0044]

[0045] In equations (1) and (2), u and v are frequency domain coordinates, M and N are the number of pixels in the x and y directions of the image, and i is an imaginary unit. The Fourier transform results of the images f(x,y), g′(x,y) and the mask images m1(x,y), m′2(x,y) are F(u,v), G′(u,v), M1(u,v), and M′2(u,v), respectively. The normalized cross-correlation function ψ between the reference image and the image to be processed is defined as:

[0046]

[0047] The temporary variables are:

[0048]

[0049]

[0050]

[0051] In the above equations (4)-(6), the symbol · represents the multiplication of the corresponding elements of the image matrix. Represents the new matrix obtained by multiplying the corresponding elements of matrix F and matrix G, and performing inverse Fourier transform on this new matrix. Represents the new matrix obtained by multiplying the corresponding elements of the matrix F and the matrix M′2, and performing the inverse Fourier transform on this new matrix; Represents the new matrix obtained by multiplying the corresponding elements of the matrix M1 and the matrix G', and performing the inverse Fourier transform on this new matrix; Represents the new matrix obtained by multiplying the corresponding elements of the matrices M1 and M2', and performing the inverse Fourier transform on this new matrix; It represents the Fourier transform of the corresponding elements of the matrix f and the matrix f, and then the new matrix obtained by multiplying it with the matrix M′2, and the inverse Fourier transform of this new matrix; It means that the matrix g' is multiplied by the corresponding elements of the matrix g', and then the Fourier transform is performed, and the new matrix is ​​multiplied by the matrix M1, and the inverse Fourier transform is performed on this new matrix;

[0052] The ψ calculated by equation (3) is a matrix of (2M-1, 2N-1) with a value range of [-1, 1], where 1 indicates complete correlation and -1 indicates complete irrelevance. Find the position where ψ takes the maximum value, and its offset from the center point of the matrix is ​​the offset between the two images f(x, y) and g(x, y).

[0053] Among them, the mask images m1(x, y) and m2(x, y) can be detected by using the edge detection Candy algorithm to obtain the edge of the experimental model and then perform morphological dilation operation to determine it.

[0054] The image registration method for the temperature-sensitive paint wind tunnel experiment of the present invention has the following specific steps:

[0055] 1) Use the high-speed camera of the temperature-sensitive paint measurement system to obtain the temperature-sensitive paint experimental image, and determine the reference image f(x, y) and the image g(x, y) to be processed;

[0056] 2) Use the Candy edge detection algorithm to determine the edge of the experimental model and perform binarization, then perform morphological dilation operation to determine the mask images m1(x, y) and m2(x, y) of the reference image and the image to be processed respectively;

[0057] 3) Rotate the image to be processed g(x, y) and its mask image m2(x, y) 180 degrees clockwise to obtain new images g′(x, y) and m′2(x, y);

[0058] 4) Perform Fourier transform on the images f(x,y), g′(x,y), m1(x,y), and m′2(x,y) to obtain F(u,v), G′(u,v), M1(u,v), and M′2(u,v). The calculation principle is as shown in formula (1) above (the result of Fourier transform of the corresponding image matrix can be regarded as a temporary variable for calculating ψ);

[0059] 5) Define the normalized cross-correlation function ψ between the reference image and the image to be processed as shown in the above formula (3), and determine the offset between the position where it takes the maximum value and the center point of the ψ matrix to determine the offset between f(x, y) and g(x, y).

[0060] Application examples are as follows:

[0061] Figure 1 This is a wind tunnel test model for temperature-sensitive paint. Figure 2 This is a typical experimental image of temperature-sensitive paint. In the experiment, multi-camera and multi-angle shooting were used to achieve full model measurement. Figure 2 This is the result of shooting with one of the cameras. Figure 3 The edge of the experimental model identified by the Candy algorithm is Figure 4 It is the mask image determined by morphological dilation operation after the model edge image is binarized; Figure 5 is the heat flux density distribution on the model surface calculated after registration; Figure 6 is the surface heat flux density distribution of the model obtained by direct processing without registration. Figure 5 、 6A comparison shows that direct processing without registration will produce significant errors at the model's edges. Because the model in this experiment moved to the upper right in the field of view, the heat flux density at the upper edge of the model was significantly higher than that at the lower edge of the model. The method described in this patent effectively eliminates the effects of model vibration, achieving sub-pixel registration and ensuring the accuracy of the temperature-sensitive paint experimental results.

[0062] The present invention utilizes the contour features of the experimental model itself to achieve sub-pixel precision registration of the temperature-sensitive paint image, which can avoid the need to make marking points and give full play to the advantages of the temperature-sensitive paint technical surface measurement.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A temperature-sensitive paint wind tunnel test image registration method, characterized in that ,The registration method includes the following steps: 1) Obtain images captured during the temperature-sensitive paint experiment and determine the reference image f(x, y) and the image g(x, y) to be processed; 2) Use the Candy edge detection algorithm to determine the edge of the experimental model and perform binarization processing, and then perform morphological dilation operation to determine the mask images m1(x, y) and m2(x, y) of the reference image and the image to be processed respectively; 3) Rotate the image to be processed g(x, y) and its mask image m2(x, y) 180 degrees clockwise to obtain new images g′(x, y) and m2′(x, y); 4) After Fourier transforming the images f(x,y), g′(x,y), m1(x,y), and m2′(x,y), the Fourier transform results are F(u,v), G′(u,v), M1(u,v), and M2′(u,v), respectively; 5) Define the normalized cross-correlation function ψ between the reference image and the image to be processed and determine the offset between the position where it takes the maximum value and the center point of the ψ matrix. This offset is the offset between f(x,y) and g(x,y); The process of defining the normalized cross-correlation function of the reference image and the image to be processed in step 5) is as follows: First define represents the inverse Fourier transform, and then the temporary variables are calculated by the following formulas (4)-(6) In the above formulas (4)-(6), · represents the multiplication of the corresponding elements of the image matrix, Represents the new matrix obtained by multiplying the corresponding elements of matrix F and matrix G, and performing inverse Fourier transform on this new matrix. Represents the new matrix obtained by multiplying the corresponding elements of the matrix F and the matrix M2′, and performing the inverse Fourier transform on this new matrix; Represents the new matrix obtained by multiplying the corresponding elements of the matrix M1 and the matrix G', and performing the inverse Fourier transform on this new matrix; Represents the new matrix obtained by multiplying the corresponding elements of the matrices M1 and M2', and performing the inverse Fourier transform on this new matrix; It represents the Fourier transform of the corresponding elements of the matrix f and the matrix f, and then the new matrix obtained by multiplying it with the matrix M2′, and the inverse Fourier transform of this new matrix; It means that the matrix g' is multiplied by the corresponding elements of the matrix g', and then the Fourier transform is performed, and the new matrix is ​​multiplied by the matrix M1, and the inverse Fourier transform is performed on this new matrix; Finally, the normalized cross-correlation function ψ of the reference image and the image to be processed is defined as: The normalized cross-correlation function ψ calculated by equation (3) is a matrix of (2M-1, 2N-1) with a value range of [-1, 1], where 1 indicates complete correlation and -1 indicates complete non-correlation. Find the position where ψ takes the maximum value, and its offset from the center point of the matrix is ​​the offset between the two images f(x, y) and g(x, y).

2. The temperature-sensitive paint wind tunnel test image registration method according to claim 1, characterized in that : The mask images m1(x, y) and m2(x, y) in step 2) are detected using the edge detection Candy algorithm to obtain the edge of the experimental model and then determined by morphological dilation operation; and the mask images m1(x, y) and m2(x, y) are binary images, the areas involved in the calculation are assigned a value of 1, and the other areas are assigned a value of 0.

3. The temperature-sensitive paint wind tunnel test image registration method according to claim 1, characterized in that: The specific process of step 4) is as follows: definition represents Fourier transform, and the calculation process of Fourier transform and inverse transform is shown in the following equations (1)-(2); In formulas (1) and (2), u and v are frequency domain coordinates, M and N are the number of pixels in the x and y directions of the image, and i is the imaginary unit; The Fourier transform results corresponding to the images f(x,y), g′(x,y), m1(x,y), and m2′(x,y) are F(u,v), G′(u,v), M1(u,v), and M2′(u,v), respectively.

4. The image registration method for temperature-sensitive paint wind tunnel experiments according to claim 1, characterized in that: In the step 1), a high-speed camera of the temperature-sensitive paint measurement system is used to capture images of the temperature-sensitive paint experiment.

Images