Refractive index measurement method based on digital image correlation and inversion method

Abstract

A refractive index measurement method based on digital image correlation and inversion methods simultaneously captures a top-view speckle image and a side-view profile image of a droplet placed on a speckle pattern. Based on the principles of geometric optics, a spatial scale consistency conversion is established between the two. A segmentation-assisted digital image correlation algorithm is used to extract the top-view speckle displacement caused by refraction. An edge detection algorithm is used to extract the curvature parameters of the droplet's sidewall profile. A comprehensive physical model is then established to determine the droplet's refractive index at any refraction point. Finally, a particle swarm optimization algorithm is used to iteratively optimize the incident angle and refraction parameters of the droplet's profile feature points to obtain the optimal refraction solution. This method eliminates the need for other standard refractive index liquids as a calculation reference, avoiding reliance on a benchmark refractive index and improving the ability to cope with dynamic variable interference in practical engineering applications.

Description

Technical Field

[0001] The present invention relates to a technology in the field of optical measurement, in particular to a refractive index measurement method based on a digital image correlation method and an inversion method. Background Art

[0002] Existing technology for measuring the refractive index of micro-transparent droplets relies on highly precise optical instruments and is based on the assumption of an ideal steady-state environment. It is difficult to cope with the interference of dynamic variables such as temperature and concentration changes on the measurement results, and it is difficult to achieve real-time and effective monitoring of the dynamic changes in the refractive index of the droplets. Summary of the Invention

[0003] In response to the problems of the existing technology in accurately characterizing the refractive index of transparent droplets, such as the reliance on precision optical devices and insufficient dynamic process monitoring capabilities, the present invention proposes a refractive index measurement method based on digital image correlation method and inversion method. The imaging optical path is described in the form of a refraction model, avoiding the problems of measurement accuracy or failure caused by the discrepancy between actual conditions and assumptions in the existing methods, thereby significantly improving the measurement accuracy and practicality of the method. The physical model established based on geometric optics is used for calculation, and only the refractive index of air at the measurement temperature is taken as a calculation reference. Other standard refractive index liquids are not required as calculation references, thereby avoiding dependence on the benchmark refractive index and improving the ability to cope with dynamic variable interference in actual engineering.

[0004] The present invention is achieved through the following technical solutions:

[0005] The present invention relates to a refractive index measurement method based on digital image correlation and inversion methods. By synchronously collecting a top-view speckle image and a side-view profile image of a droplet placed on a speckle pattern, a spatial scale consistency conversion between the two is established based on the principles of geometric optics. A segmentation-assisted digital image correlation (SA-DIC) algorithm is then used to extract the top-view speckle displacement caused by refraction. An edge detection algorithm is then used to extract the curvature parameters of the droplet sidewall profile. A comprehensive physical model is then established to obtain the droplet refractive index corresponding to any refraction point. Finally, a particle swarm optimization (PSO) algorithm is used to iteratively optimize the incident angle and refraction parameters of the droplet profile feature points to obtain the optimal refraction solution.

[0006] The droplet to be measured is a transparent and uniform droplet that may contain moderate color. However, it should be ensured that there is no turbidity caused by crystal precipitation or undissolved matter inside the droplet when measuring under dynamic environmental changes, so as to avoid reducing the clarity and recognizability of the speckle pattern transmission, thereby ensuring the accuracy of displacement calculation.

[0007] The contact angle of the droplet to be measured should be greater than 5° and less than 90°. Excessively large contact angles will cause secondary refraction in the speckle pattern captured from a top-down perspective, significantly deforming the speckle pattern in the image and increasing errors in the calculated results. However, if the contact angle is too small, the droplet's side profile loses its regular arc shape due to the interaction between the droplet's surface tension and the three-phase interface, making it difficult to accurately fit the droplet's profile in the side-view image with a smooth curve. This, in turn, significantly increases errors in the refractive index calculation. Therefore, appropriately controlling the contact angle range not only reduces the effects of refraction and deformation during image acquisition but also significantly improves the accuracy of refractive index and displacement measurements.

[0008] The speckle pattern is preferably prepared by inkjet printing on offset paper and polyvinyl chloride plastic sealing technology, which can provide good speckle clarity and recognition at a magnification that ensures clear droplet edges, thereby improving the accuracy of subsequent displacement calculations.

[0009] The top-view speckle image and side-view profile image are acquired via a dual-view coupled imaging system comprising a top-view camera and a side-view camera. The top-view camera utilizes a transmissive imaging mode. Transmitted light from the characteristic surface speckle pattern is incident from a planar backlight source through the bottom of the droplet. After penetrating the droplet and undergoing refraction at the liquid-air interface, the top-view camera ultimately records the difference in speckle image between the presence and absence of the droplet. The side-view camera captures backlit profile side-view images generated by double refraction from a vertical planar light source through the surface of the liquid being measured. This clearly records subtle changes in the droplet's side geometry under different conditions, thereby enabling precise capture and analysis of the droplet's dynamic behavior and physical parameters. To ensure the integrity and accuracy of droplet edge identification, the camera's top-view field of view is vertical, perpendicular to the horizontal speckle plane supporting the droplet, and covers the droplet's complete circular outline. The side-view field of view is horizontally aligned with the center of the speckle plane, covering the droplet's entire semicircular edge.

[0010] The top view image acquisition is configured with bottom light source transmission illumination to ensure the clarity and recognizability of the speckle pattern; the side view image acquisition adopts a planar backlight illumination method to avoid the characteristic surface reflection problem caused by projection lighting, which can enhance the side view contour contrast to improve the contour recognition accuracy.

[0011] The top-view camera and the side-view camera use the same frequency for image acquisition. That is, the two cameras use a unified sampling frequency and a precise trigger signal to synchronously record the changes in the droplet morphology and speckle pattern during the dynamic process, ensuring a strict correspondence between the recorded images on the time axis, thereby achieving accurate parameter calibration and high-fidelity restoration of the droplet dynamic morphology.

[0012] The geometrical optics principle specifically includes:

[0013] 1) Calibrate the top-down camera to obtain the camera's internal parameters, including focal length, optical center position, and lens distortion coefficient.

[0014] 2) Based on the correspondence between the pixel coordinates of the calibration plate feature points extracted from the top-view image and the actual spatial points, the actual physical size of the droplet contour in the top-view state is accurately determined, and a two-dimensional spatial scale mapping relationship of the top-view image is established.

[0015] 3) The side-view images taken at the same moment are analyzed and fitted to extract the geometric features of the droplet's side profile. The droplet diameter measured from the side-view image is matched with the droplet diameter at the corresponding moment in the top-view image, and a spatial association is established between the two cameras. This allows for consistent conversion of spatial scales between the two cameras, allowing image information obtained from different perspectives to be accurately unified into the same spatial coordinate system, effectively improving the accuracy and stability of the droplet geometric parameter measurement.

[0016] The calibration described herein adopts but is not limited to the Zhang Zhengyou calibration method.

[0017] The segmentation-assisted digital image correlation algorithm (SA-DIC) specifically includes:

[0018] A) Segment the top-view speckle image after consistency conversion to divide the image into multiple sub-regions with different characteristics. More accurate feature matching is achieved within each sub-region based on its characteristics, addressing the displacement discontinuity problem that is easily found when measuring droplet boundaries;

[0019] B) The speckle patterns of the surface under test under different conditions are compared with a reference image. Grayscale matching is used to find the correspondence between points in the subregions of the image, thereby obtaining surface displacement information. Specifically, in displacement field measurement, a DIC algorithm based on multi-subregion segmentation is used. After image segmentation, the corresponding positions in the reference image and the target image are determined through point-by-point matching for each subregion.

[0020] The point-by-point matching uses the zero-mean normalized least squares function (ZNSSD) to describe the matching degree of each sub-region in different images, specifically:

[0021] Where: average grayscale value of reference sub-region

[0022] Average grayscale value of target sub-region f(x,y) represents the grayscale value of the middle point P(x,y) in the reference subregion when matching a single subregion, g(x′,y′) represents the grayscale value of P′(x′,y′), and p is the deformation parameter vector from the reference subregion to the target subregion.

[0023] The smaller the correlation coefficient C(p), the more reliable the match between the reference subregion and the target subregion. Based on this, the displacement of the target region is inferred by establishing a geometric mapping relationship by finding the deformation parameter vector p that minimizes the correlation coefficient C(p). For adjacent subregions, a joint optimization is performed incorporating displacement continuity constraints, effectively reducing local matching errors.

[0024] The edge detection algorithm uses an improved Canny edge detection algorithm to construct an image edge detection model to achieve accurate extraction of edge information in the side view image of the droplet to be tested. It uses but is not limited to the technology described in "An improved Canny edge detection algorithm" published by RONG W et al. in 2014.

[0025] The physical model described above is based on the actual refractive optical path structure and is used to establish a quantitative relationship between the refractive index of the liquid being measured and the displacement of the droplet profile and speckle base image. To accurately quantify the refractive index of a transparent droplet, the precise speckle displacement, the side-view droplet profile, and the refractive indices of the media through which the imaging light passes from both perspectives are all factored into the refractive model. Calibration methods are then used to obtain more precise model parameter values, significantly improving measurement accuracy.

[0026] The comprehensive physical model mentioned above means that the characteristic surface where the droplet is located is used as the reference plane, and any maximum contour section of the droplet is selected for analysis. Assuming that the plane where the maximum contour section is located is the XOZ coordinate plane, and its origin is the projection coordinate position from the center of the target surface of the industrial camera lens to the base plane of the droplet, then any refraction point R(x f ,z f )’s coordinates satisfy: Among them: Z0(0,z0) is the optical center of the lens in the pinhole imaging, R(x f ,z f ) is the coordinate position of any refraction point on the maximum profile section; when there is no droplet on the substrate at a certain point on the imaging target surface, point R on the X-axis corresponds to the actual spatial point X1(x1,0); when there is a droplet, the corresponding spatial point is the image point X′1(x′1,0). The height z0 of the lens optical center in pinhole imaging is obtained by Zhang Zhengyou's camera calibration method. The mathematical expression of the maximum profile f(x,z) is obtained by the improved Canny edge detection algorithm. The correspondence between the two points X1 and X′1 can be determined by the DIC matching relationship; the refractive index of the droplet corresponding to any refraction point is then obtained as n sin γ = n α sin α, where: angle of incidence of refraction Refraction angle Object point X1, image point X′1 and refraction point R(x f ,z f) is the angle θ, n α is the refractive index of air at a fixed temperature, the vector vector vector

[0027] During the calculation process, a feature point matching algorithm is used to extract numerous feature points from the images of the droplet before and after refraction. However, only those located on the maximum side profile are selected for the subsequent refractive index calculation. Because the displacement of each feature point conforms to the refraction model, a refractive index value can be calculated for each feature point individually. However, the refractive index calculation for a single feature point is susceptible to the combined effects of SA-DIC measurement errors, edge detection errors, and errors in the position of the refraction point, and can result in significant deviations. Directly assuming a global average value would ignore the differences in data quality among the feature points, leading to averaging of errors and reduced final measurement accuracy.

[0028] The particle swarm optimization algorithm (PSO) is used to iteratively optimize the incident angle and refractive index parameters of the droplet profile feature points. The method includes: selecting s feature points to optimize and calculate the refractive index, initializing the particle position and velocity at the beginning of the calculation and defining the upper and lower bounds of the search space, treating the refractive index of the s feature points as the position of the particle to be optimized and updating it in each iterative calculation, and evaluating the refractive index according to the fitness function, specifically: the updated particle velocity v i (t+1)=w(t)·v i (t)+c1·r1·(pBest i -x i )+c2·r2·(gBest i -x i ), updated particle position x i (t+1)=x i (t)+v i (t+1), where: w(t) is the inertia weight that controls the influence of the particle's current velocity on the update; c1 and c2 are the learning factors of the individual and group; r1 and r2 are random factors that determine the randomness of the search; pBest i gBest is the best historical position of the particle, that is, the refractive index calculated at a single refraction point; i is the global optimal position, that is, the refractive index value obtained according to the weight; x i The current position of the particle is calculated. In the optimization process, the goal of each particle is to minimize the fitness function F(x), which takes into account the differences in the incident and exit angles related to the refractive index and assigns weights according to the situation at each point.

[0029] The fitness function Among them: the weighted coefficient of each angle on fitness maxw Represents the parameters that control the weight of the incident angle and refraction angle, sin in (i) sin out (i) are the incident cosine and exit cosine of the i-th feature point, and x is the refractive index value to be optimized.

[0030] The evaluation refractive index is: through the particle swarm optimization algorithm, the particle speed and position are continuously iterated. When the iterative change of the global fitness value F(x) is less than the set threshold, the optimization result is judged to be converged. When the optimization termination condition is met, the iteration stops. The v i The solution inversion of (t+1) obtains the global optimal position gBest, that is, the best refractive index solution n, specifically: n = argmin [F (x i ,sin in ,sin out ,α,γ,w(i),…)], F(x)-F(x) prev <ε, where: F(x) prev is the fitness function result of the last optimization; ε is the set threshold.

[0031] The threshold used in the present invention is ε=10 -6 .

[0032] Preferably, in the process of setting the fitness weighting coefficient, a smaller weight coefficient is assigned to the area closer to the center of the droplet, given that the refraction angle change gradient is larger; conversely, a larger weight coefficient is used in the far field area because the refraction angle stability is enhanced.

[0033] Technical Effects

[0034] This method uses a comprehensive refraction physics model based on geometric optics and the law of refraction to obtain a droplet's top-view speckle image and side-view profile image. It then employs segmentation-assisted digital image correlation (SA-DIC) technology and an improved Canny edge detection algorithm to accurately quantify the speckle displacement field and droplet profile curvature characteristics caused by refraction. Furthermore, a particle swarm optimization (PSO) algorithm is used to iteratively optimize the incident angle and refraction parameters in the physical model. Compared to existing methods that rely on precision optical components, static measurement conditions, and a reference refractive index liquid, this method effectively improves the accuracy, stability, and environmental adaptability of transparent droplet refractive index measurements, achieving a relative measurement error of no more than 0.217%, achieving the accuracy level of a laboratory-grade refractometer. Furthermore, it enables real-time, non-invasive monitoring of the dynamic changes in the refractive index of trace amounts of liquid. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Flowchart of the present invention;

[0036] Figure 2Schematic diagram of an embodiment scenario;

[0037] Figure 3 Schematic diagram of the embodiment system. DETAILED DESCRIPTION

[0038] like Figure 2 As shown, this embodiment relates to a refractive index measurement device based on a digital image correlation method and an inversion method. The device comprises a computing unit (not shown), two industrial cameras 1 and 2 connected thereto, a speckle substrate 5, a planar light source 7, a backlight source 8, and a stage 6. The stage 6 is adjusted to a horizontal position and provided with a planar light source. The speckle substrate 5 is placed on the stage and is stationary. A droplet 3 to be measured is placed on the plane of the speckle substrate using a pipette using the hanging drop method. The two industrial cameras are positioned directly above and horizontally to the side of the droplet to be measured, respectively. The planar light source 7 and backlight source 8 are positioned behind the droplet to be measured in the direction of the side and top view cameras, respectively, enabling the two industrial cameras to perform dual-perspective imaging of the droplet to be measured. The computing unit receives and processes image data collected by the dual-perspective imaging system and calculates the refractive index of the droplet to be measured based on a pre-established physical model.

[0039] The speckle substrate is made of a light-transmitting material and is illuminated by a planar light source below, so that the industrial camera lens in the downward direction can clearly collect image information of the droplets, thereby ensuring measurement accuracy.

[0040] In this embodiment, the droplet 3 to be measured is distilled water. Measurements should be made after the droplet contacts the substrate and its morphology stabilizes. The quartz substrate 4, speckle substrate 5, and stage 6 are all light-transmissive. Together with the back-mounted planar light source 7, this allows for clear imaging of the droplet 3 even in relatively dark environments, ensuring measurement accuracy.

[0041] like Figure 1 As shown in FIG. 1 , a method for measuring the dynamic change of the refractive index of a transparent droplet based on the above-mentioned device in this embodiment is shown, which specifically includes:

[0042] S1. Build the measuring device.

[0043] S2. Clean the stage and fix the quartz substrate 4 and the speckle substrate 5, and place the droplet 3 to be measured on the speckle substrate 5 as a hanging drop.

[0044] S3. Based on the measurement requirements, industrial cameras 1 and 2 are started to respectively capture the dynamic morphological changes of the droplet to be measured as it evaporates over time from two perspectives.

[0045] S4. After the dynamic continuous capture is completed, the original speckle pattern without the droplets to be measured is captured using the original camera parameters of industrial cameras 1 and 2. The top-view lens is calibrated using a calibration plate to obtain the internal parameters of industrial camera 1. The camera calibration method used in this embodiment is the Zhang Zhengyou calibration method. By acquiring multiple poses using the calibration plate's in-plane translation, elevation, and rotation, a mapping relationship between the extrinsic parameter translation matrix and the lens optical center height is established to determine the parameter z0.

[0046] S5. Compare and analyze the speckle pattern under the test droplet, captured continuously by industrial camera 1, with the original speckle pattern 5. Using the fully evaporated image as the DIC reference image and the images captured at each moment as the target image, the SA-DIC method is used to calculate the speckle displacement. By calculating the image displacement before and after refraction at the characteristic surface point, the displacement field near the maximum outline in the top view is obtained.

[0047] S6. Use the side view image acquired synchronously by industrial camera 2 at each moment as the target image for contour recognition, obtain a continuous and smooth arc contour through edge detection, and establish a complete geometric model including arc segments (side edges of the droplet) and straight line segments (base plane) and its mathematical expression.

[0048] S7. Calculating the refractive index of the droplet to be measured using computer 9, specifically including:

[0049] 1) An appropriate number of refraction points are uniformly selected on the identified edge contour according to the calculation accuracy and efficiency requirements.

[0050] 2) Based on the numerical calculation framework constructed by the established physical model, the single-point refractive index value corresponding to the refraction point selected in step 1) is calculated point by point.

[0051] 3) Combine the particle swarm optimization (PSO) algorithm to perform parameter inversion and obtain the optimized optimal refractive index.

[0052] 4) Based on the refractive index data of the droplet to be measured calculated at each moment, the time-refractive index change relationship of the droplet to be measured during the dynamic change process is obtained.

[0053] Experimental results demonstrate the feasibility of the established physical model using a camera with a resolution of 3648 × 5472 pixels. A 0.25 mm × 0.25 mm checkerboard calibration plate (5 mm × 5 mm overall) was used for camera calibration. To verify the feasibility of the established physical model, 10 μL of sodium chloride solution with mass fractions of 0%, 5%, 10%, 15%, and 20% was used as the experimental fluid. The evaporation process was recorded at a constant temperature of 20 ± 0.5°C with a synchronous sampling rate of 10 seconds per frame until the fluid completely evaporated or crystallized. The completely evaporated image served as the DIC reference image, and the images captured at each moment served as the target image. A 20-pixel sampling interval was set, and the displacement of the image before and after refraction of the feature surface point was calculated to obtain the displacement field near the maximum contour. The side view images captured at each moment served as the target image for contour recognition. The contours and displacements obtained through canny edge detection were combined with the physical model and the relationship between refractive index and concentration to analyze the dynamic changes in the refractive index and concentration of the target liquid.

[0054] As shown in Table 1, the maximum relative error between the refractive index measured by this method and the standard values ​​of various classical methods is 2.196%, which proves that this method has high practical accuracy in the concentration range of 0-20%, and achieves the purpose of low-cost, high-precision, dynamic measurement of refractive index.

[0055] Table 1

[0056]

[0057] Compared with the existing technology, the present invention does not require complex optical devices and can complete real-time online monitoring of trace droplets using only conventional imaging equipment. Its non-invasive nature avoids interference with the sample and is suitable for long-term dynamic research on trace liquids (microliters), providing a convenient and low-cost measurement solution for in situ analysis of the physical and chemical properties of solutions.

[0058] In summary, this method eliminates the defects of traditional technologies in that the measurement accuracy is affected by dynamic environmental interference and dependence on optical devices, and can complete real-time online monitoring of trace droplets using only conventional imaging equipment. While ensuring the advantages of non-contact measurement, the relative error of the measurement results compared with the standard values ​​of classical measurement methods is ≤0.217%, and its accuracy level is close to that of laboratory-grade refractometers, meeting the requirements of actual engineering measurements. This method can directly track the dynamic changes of the refractive index and concentration of the solution during evaporation, mixing and chemical reactions. Its non-invasive nature avoids interference with the sample and is suitable for long-term dynamic studies of trace liquids (microliters), providing a convenient and low-cost measurement solution for in situ analysis of the physical and chemical properties of the solution.

[0059] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principles and purpose of the present invention. The scope of protection of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. All implementation schemes within its scope shall be subject to the constraints of the present invention.

Claims

1. A refractive index measurement method based on digital image correlation method and inversion method, characterized in that: By synchronously acquiring a top-view speckle image and a side-view profile image of a droplet placed on a speckle pattern, a spatial scale consistency conversion between the two is established based on the principles of geometric optics. The top-view speckle displacement caused by refraction is extracted using a segmentation-assisted digital image correlation (SA-DIC) algorithm, and the curvature parameters of the droplet sidewall profile are extracted using an edge detection algorithm. A comprehensive physical model is then established to obtain the droplet refractive index corresponding to any refraction point. Finally, a particle swarm optimization (PSO) algorithm is used to iteratively optimize the incident angle and refraction parameters of the droplet profile feature points to obtain the optimal refraction solution. The comprehensive physical model is as follows: taking the characteristic surface of the droplet as the reference plane, selecting any maximum contour section of the droplet for analysis, assuming that the plane where the maximum contour section is located is the XOZ coordinate plane, and its origin is the projection coordinate position of the center of the target surface of the industrial camera lens from the top view to the base plane of the droplet, then any refraction point The coordinates satisfy: ,in: is the optical center of the lens in pinhole imaging, is the coordinate position of any refraction point on the maximum profile section; when there is no droplet on the substrate at a certain point on the imaging target surface, the point The object point on the X-axis corresponds to the actual space point , when there is a droplet, the corresponding space point is the image point , the height of the optical center of the lens in pinhole imaging Obtained by Zhang Zhengyou's camera calibration method, maximum contour The mathematical expression of is obtained by the edge detection algorithm, two points and The corresponding relationship is determined by the matching relationship of DIC; then the refractive index of the droplet corresponding to any refraction point is obtained , refraction incident angle , refraction angle , object point , image point and refraction point The angle formed is , is the refractive index of air at a fixed temperature, the vector ,vector ,vector ; The segmentation-assisted digital image correlation algorithm specifically includes: A) Segmenting the top-view speckle image after consistency conversion into multiple sub-regions with different characteristics, and achieving more accurate feature matching within each sub-region based on its characteristics; B) Comparing the speckle patterns of the surface under test in different states with a reference image, using grayscale matching to find the correspondence between points in the subregions of the image, thereby obtaining surface displacement information. Specifically, in displacement field measurement, a DIC algorithm based on multi-subregion segmentation is used. After image segmentation, for each subregion, point-by-point matching is performed to obtain the corresponding position in the reference image and the target image; The point-by-point matching uses a zero-mean normalized least squares function to describe the matching degree of each sub-region in different images, specifically: , where: the average grayscale value of the reference sub-region , the average grayscale value of the target sub-region , Represents the midpoint of the reference subregion when matching a single subregion The gray value of represent The gray value of is the deformation parameter vector from the reference sub-region to the target sub-region; By solving the correlation coefficient Minimized deformation parameter vector , establish a geometric mapping relationship to infer the displacement value of the target area; for adjacent sub-regions, joint optimization is performed in combination with displacement continuity constraints, thereby effectively reducing local matching errors.

2. The refractive index measurement method based on digital image correlation method and inversion method according to claim 1 is characterized in that: The top-view speckle image and side-view profile image are acquired using a dual-view coupled imaging system comprising a top-view camera and a side-view camera. The top-view camera's optical path utilizes a transmissive imaging mode. Transmitted light from the characteristic surface speckle pattern is incident from a planar backlight source through the bottom of the droplet. After penetrating the droplet and undergoing refraction at the liquid-air interface, the top-view camera ultimately records the difference in speckle images between the presence and absence of the droplet. The side-view camera acquires backlit profile side-view images generated by two refractions from a vertical planar light source through the surface of the liquid to be measured. This images records subtle changes in the droplet's side geometry under different conditions, thereby enabling precise capture and analysis of the droplet's dynamic behavior characteristics and physical parameters.

3. The refractive index measurement method based on digital image correlation method and inversion method according to claim 1 is characterized in that: The geometrical optics principle specifically includes: 1) Calibrate the top-down camera to obtain its internal parameters, including focal length, optical center position, and lens distortion coefficient; 2) Based on the correspondence between the pixel coordinates of the calibration plate feature points extracted from the top-view image and the actual spatial points, the actual physical size of the droplet contour in the top-view state is accurately determined, and a two-dimensional spatial scale mapping relationship of the top-view image is established; 3) Analyze and fit the side-view images taken at the same moment to extract the geometric features of the droplet's side profile. The droplet diameter measured from the side-view image is matched with the droplet diameter at the corresponding moment in the top-view image, and a spatial association is established between the two cameras. This allows for consistent conversion of spatial scales between the two cameras, allowing image information obtained from different perspectives to be accurately unified into the same spatial coordinate system.

4. The refractive index measurement method based on digital image correlation method and inversion method according to claim 1 is characterized in that: Among the feature points extracted from the images before and after the droplet refraction by the feature point matching algorithm, the feature points located on the maximum side view contour are involved in the subsequent refractive index calculation.

5. The refractive index measurement method based on digital image correlation method and inversion method according to claim 1 is characterized in that: The particle swarm optimization algorithm is used to iteratively optimize the incident angle and refraction parameters of the droplet profile feature points. The refractive index is calculated by optimizing the feature points. At the beginning of the calculation, the particle position and velocity are initialized and the upper and lower bounds of the search space are defined. The refractive index of each feature point is regarded as the position of the particle to be optimized and updated, and the refractive index is evaluated according to the fitness function, specifically: the particle speed after update , updated particle position ,in: To control the inertia weight of the particle's current velocity on the update; 、 Learning factors for individuals and groups; 、 A random factor that determines the randomness of the search; is the refractive index calculated at the best historical position of the particle, that is, a single refraction point; is the global optimal position, that is, the refractive index value obtained according to the weight; To calculate the current position of the particle, in the optimization process, the goal of each particle is to minimize the fitness function , which considers the differences in incident and exit angles related to the refractive index and assigns weights to each point accordingly.

6. The refractive index measurement method based on digital image correlation method and inversion method according to claim 5 is characterized in that: The fitness function , where: the weighted coefficient of each angle on fitness , represents the parameters that control the weights of the incident and refraction angles, Respectively The incident cosine and outgoing cosine of the feature points, is the refractive index value to be optimized; It means: through the particle swarm optimization algorithm, the particle speed and position are continuously iterated. When the global fitness value When the iterative change is less than the set threshold, the optimization result is judged to be converged, and the iteration stops when the optimization termination condition is met. The global optimal position is obtained by inverting the solution , that is, the optimal refractive index solution n, specifically: , ,in: The fitness function result of the last optimization; To set the threshold.

7. The refractive index measurement method based on digital image correlation method and inversion method according to claim 6, characterized in that: In the process of setting the fitness weighting coefficient, the weight coefficient of the refraction angle in the area closer to the center of the droplet is greater than the refraction angle in the far field area.

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