An adaptive high dynamic range measurement algorithm and device based on time domain stacking

Abstract

The present application relates to the field of machine vision three-dimensional measurement, and particularly relates to a kind of adaptive high dynamic range measurement algorithm and device based on time domain stacking.The algorithm is automatically adjusted exposure time and stacking times according to the measurement scene demand by adaptive parameter selection formula, ensure measurement accuracy and avoid exposure error.The core steps include: obtaining the phase shift stripe pattern of measurement scene, calculating average white field diagram and its modulation degree and intensity proportionality coefficient;According to the white field diagram, set the desired accuracy, and calculate the best measurement parameters by adaptive parameter selection method;Finally, three-dimensional reconstruction is carried out on the scene, and the measurement result consistent with the desired accuracy is obtained.The present application also provides a corresponding measurement device, including projection module, image acquisition module and data processing module, can efficiently and accurately measure the three-dimensional topography in high dynamic range scene, significantly improve the measurement efficiency and accuracy, and optimize the resource utilization.

Description

Technical Field

[0001] The present invention relates to the field of machine vision three-dimensional measurement and calculation technology, and in particular to an adaptive high dynamic range measurement algorithm and device based on time domain stacking. Background Art

[0002] In recent years, non-contact measurement methods have gained increasing attention in industry and scientific research. Fringe projection technology, with its high precision, fast response, and flexible adaptability, has particularly stood out in the field of optical 3D measurement. This technology uses a projector to project a specific fringe pattern onto the surface of the object being measured. The fringe pattern deforms according to the object's geometry. This deformed fringe pattern is captured by a high-precision camera. After careful phase demodulation and system calibration, the object's 3D topography can be accurately reconstructed.

[0003] Fringe projection profilometry (FPP) typically involves projecting one or more sinusoidal light fringes onto an object's surface. The object's three-dimensional topography is then calculated by analyzing the deformed fringe pattern captured by a camera. As its application scope expands, more and more scenarios are emerging, such as high dynamic range (HDR) scenes. Due to the large variations in reflectivity in HDR scenes, the brightness of the captured deformed fringes varies significantly. Traditional fringe projection applications use an 8-bit (256 grayscale levels) image format, which means the image can only represent a limited range of brightness. For areas with high reflectivity, conventional camera exposure settings can overexpose them, resulting in a loss of detail in the image. Conversely, for areas with low reflectivity, underexposure can render them virtually invisible. When measuring HDR scenes, regions of varying reflectivity require different exposure times to adapt to their brightness. This is difficult to achieve with traditional FPP methods using fixed parameter settings, often resulting in a loss of detail in dark areas or overexposure in bright areas, compromising the accuracy and reliability of the final measurement results. Summary of the Invention

[0004] The present invention aims to overcome the drawback of existing phase-shift profilometry (HDR) methods, which struggle to accurately predict the final measurement results. It provides an adaptive high dynamic range (HDR) measurement algorithm based on time-domain stacking. This algorithm, based on the time-domain stacking method, incorporates a phase error prediction formula to predict the achievable phase accuracy of the measurement system. By improving the signal-to-noise ratio in low-reflectivity regions, it achieves predictable 3D reconstruction of the entire field without overexposure.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] An adaptive high dynamic range measurement algorithm based on time domain stacking includes the following steps:

[0007] S1. Obtain a phase-shift fringe pattern of a measurement scene, and obtain an average white field pattern of the measurement scene; and obtain a proportional coefficient based on the modulation depth and intensity of the average white field pattern.

[0008] S2. selecting a region of interest of the measurement scene according to the average white field image;

[0009] S3. Setting a desired accuracy for the region of interest, and obtaining optimal parameters for measuring the region of interest through an adaptive parameter selection method;

[0010] S4. Reconstruct the measurement scene according to the optimal parameters to obtain an actual measurement result with the same accuracy as the expected accuracy.

[0011] Preferably, a set of phase-shift fringe patterns are captured at different exposure times for a HDR scene to be tested, and the average white field pattern is obtained according to the following formula:

[0012]

[0013] Among them, I w (x,y) represents the intensity of the pixel coordinate point (x,y) in the white field image, I n (x,y) represents the intensity of the pixel coordinate point (x,y) at the nth phase shift, n represents the number of stacking times in the time domain stacking method, and N represents the number of phase shift steps in the phase shift method.

[0014] Preferably, step S1 includes obtaining the modulation index,

[0015]

[0016] Wherein, B(x,y) represents the modulation degree of the pixel coordinate point (x,y); the proportional coefficient i is obtained according to the modulation degree and intensity of the average white field image, where

[0017] Preferably, step S2 of selecting the region of interest involves drawing a grayscale histogram of the average white field image and selecting the cluster with the lowest grayscale value as the region of interest.

[0018] Preferably, the adaptive parameter selection method further includes obtaining the inherent parameters of the measurement system, and substituting the inherent parameters into the parameter selection formula as follows:

[0019]

[0020] Obtain the optimal fringe projection intensity I under the measurement scenario opt ; Where K is the gain parameter of the camera, C is the intrinsic parameter related to the internal noise of the camera, is the desired accuracy of the setting; the optimal fringe projection intensity I opt Substitute into the following formula to find the optimal exposure time t opt :

[0021]

[0022] Among them, I w , t w They represent the intensity of the pre-shot fringe pattern and the exposure time used for shooting respectively; the optimal parameters include the optimal fringe pattern projection intensity and the optimal exposure time.

[0023] Preferably, in step S4, the measurement scene is reconstructed using the optimal parameters by adopting phase shift profilometry, and a phase unwrapping method is used to perform three-dimensional morphology reconstruction.

[0024] Preferably, the phase unwrapping method is a three-frequency phase unwrapping method, in which the low-frequency and medium-frequency parts are used to guide the phase unwrapping, and the high-frequency part is used to obtain fine three-dimensional morphology information.

[0025] The present invention also provides an adaptive high dynamic range measurement device based on time domain stacking, characterized by comprising:

[0026] A projection module, used for projecting a phase-shifted fringe pattern onto a measurement scene;

[0027] An image acquisition module, configured to acquire a phase-shifted fringe image of the measurement scene at any exposure time;

[0028] The processing module is used to execute the adaptive high dynamic range measurement algorithm based on time domain stacking described in the above technical solution to calculate and obtain high dynamic range three-dimensional shape data of the measurement scene.

[0029] Preferably, the processing module includes:

[0030] Data pre-processing unit, used to calculate the average white field map and the proportional coefficient of modulation and intensity;

[0031] A parameter calculation unit, used to calculate the optimal parameters according to the set expected accuracy and the inherent parameters of the measurement system;

[0032] The reconstruction unit is used to perform three-dimensional reconstruction of the measurement scene according to the optimal parameters.

[0033] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements an adaptive high dynamic range measurement algorithm based on time domain stacking.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] The present application can avoid measurement errors caused by improper exposure time setting while ensuring the accuracy of the entire scene measurement by referencing the adaptive parameter selection formula. This method can automatically adjust the exposure time and stacking times according to specific measurement requirements, thereby adapting to different measurement environments and target objects, achieving higher measurement accuracy and flexibility. In addition, the present application significantly improves the dynamic range and signal-to-noise ratio of the measurement by combining time-domain stacking and phase error prediction formula, especially in the low reflectivity area, significantly improving the signal quality. This method not only overcomes the problem of overexposure in bright areas or insufficient exposure in dark areas that may occur in traditional technology in high dynamic range scenes, ensuring the comprehensiveness and accuracy of the measurement results, but also can predict the accuracy that the system can achieve without actual measurement, thereby providing higher predictability of the measurement results.

[0036] The preferred scheme of the present application has the following beneficial effects:

[0037] By using different exposure times to shoot a group of phase shift fringe patterns and calculating the intensity of the average white field pattern, the challenge of brightness variation in high dynamic range scenes can be effectively addressed, ensuring accurate measurement of the average brightness of the entire scene, thereby providing an accurate data basis for subsequent adaptive parameter selection and three-dimensional reconstruction.

[0038] By calculating the modulation degree and intensity proportionality coefficient of the fringe pattern, the brightness distribution of the scene can be further accurately represented, especially in the low reflectivity area. The implementation of this step ensures that the measurement system can still maintain high measurement accuracy under complex lighting conditions.

[0039] Selecting the region of interest (ROI) and selecting adaptive parameters based on it can significantly improve the measurement accuracy of the low reflectivity area, optimize the use of system resources, reduce unnecessary measurement complexity, and improve the efficiency and effectiveness of the overall system.

[0040] Obtaining the intrinsic parameters of the measurement system and combining the adaptive parameter selection formula can ensure that the system dynamically adjusts the measurement parameters according to the specific scene, maximally reduces errors and improves the consistency and stability of the measurement results.

[0041] By using the three-frequency phase unwrapping method for three-dimensional topography reconstruction, the overall reconstruction effect can be ensured while improving the ability to capture complex structures, especially when processing low-frequency and high-frequency information, achieving high-precision topography reconstruction.

[0042] The preferred scheme of the present application also significantly optimizes the use of measurement resources, reduces resource waste, and ensures optimal performance in the measurement process under a variety of dynamic range scenes through the use of fringe multiplexing technology and accurate parameter selection. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 Flow chart of the algorithm of embodiment 2 of the present application;

[0044] Figure 2 Pre-shot sinusoidal fringe of embodiment 2 of the present application Figure 3 Phase shifting diagram;

[0045] Figure 3 Pre-shot average white field diagram of embodiment 2 of the present application;

[0046] Figure 4 Gray scale distribution histogram and selection of region of interest (ROI) of embodiment 2 of the present application;

[0047] Figure 5 HDR scene reconstruction diagram and two sets of comparison result diagrams of embodiment 2 of the present application;

[0048] Figure 6 HDR scene reconstruction diagram and two sets of comparison result diagrams of embodiment 2 of the present application;

[0049] Figure 7 Schematic diagram of fringe multiplexing unfolding method of embodiment 2 of the present application;

[0050] Figure 8 Standard plane real object diagram of embodiment 3 of the present application;

[0051] Figure 9 Step line diagram of theoretical phase error and actual phase error under different exposure times of embodiment 3 of the present application;

[0052] Figure 10 Step line diagram of theoretical phase error and actual phase error under different stacking times of embodiment 3 of the present application. DETAILED DESCRIPTION

[0053] The present application will be further described in conjunction with test examples and specific embodiments. However, this should not be understood as limiting the scope of the above-mentioned subject matter of the present application to the following examples, and any technology realized based on the content of the present application falls within the scope of the present application.

[0054] Embodiment 1

[0055] The present embodiment provides an adaptive high dynamic range measurement algorithm based on time domain stacking, including the following steps:

[0056] S1, obtaining a phase shifting fringe diagram of a measurement scene, and calculating an average white field diagram of the measurement scene; calculating a proportional coefficient according to the modulation degree and intensity of the average white field diagram;

[0057] S2, selecting a region of interest of the measurement scene according to the average white field diagram;

[0058] S3. Setting a desired accuracy for the region of interest, and obtaining optimal parameters for measuring the region of interest through an adaptive parameter selection method;

[0059] S4. Reconstruct the measurement scene according to the optimal parameters to obtain an actual measurement result with the same accuracy as the expected accuracy.

[0060] Preferably, a set of phase-shift fringe patterns are taken with different exposure times for an HDR scene to be tested, and the phase-shift fringe patterns are obtained according to the exposure time.

[0061]

[0062] Get the average white field map; where I w (x,y) represents the intensity of the pixel coordinate point (x,y) in the white field image, I n (x,y) represents the intensity of the pixel coordinate point (x,y) at the nth phase shift, n represents the number of stacking times in the time domain stacking method, and N represents the number of phase shift steps in the phase shift method.

[0063] Preferably, step S1 includes obtaining the modulation index,

[0064]

[0065] Wherein, B(x,y) represents the modulation degree of the pixel coordinate point (x,y); the proportional coefficient i is obtained according to the modulation degree and intensity of the average white field image, where

[0066] Preferably, step S2 of selecting the region of interest involves drawing a grayscale histogram of the average white field image and selecting the cluster with the lowest grayscale value as the region of interest.

[0067] Preferably, the adaptive parameter selection method further includes obtaining the inherent parameters of the measurement system, and substituting the inherent parameters into the parameter selection formula as follows:

[0068]

[0069] Obtain the optimal fringe projection intensity I under the measurement scenario opt ; Where K is the gain parameter of the camera, C is the intrinsic parameter related to the internal noise of the camera, is the desired accuracy of the setting; the optimal fringe projection intensity I opt Substitute into the following formula to find the optimal exposure time t opt :

[0070]

[0071] Among them, I w , t wThey represent the intensity of the pre-shot fringe pattern and the exposure time used for shooting respectively; the optimal parameters include the optimal fringe pattern projection intensity and the optimal exposure time.

[0072] Preferably, in step S4, the scene is reconstructed using the optimal parameters using phase shift profilometry, and a phase unwrapping method is used to reconstruct the three-dimensional morphology.

[0073] Preferably, the phase unwrapping method is a three-frequency phase unwrapping method, in which the low-frequency and medium-frequency parts are used to guide the phase unwrapping, and the high-frequency part is used to obtain fine three-dimensional morphology information.

[0074] This embodiment further provides an adaptive high dynamic range measurement device based on time domain stacking, characterized by comprising:

[0075] A projection module, used for projecting a phase-shifted fringe pattern onto a measurement scene;

[0076] An image acquisition module, configured to acquire a phase-shifted fringe image of the measurement scene at any exposure time;

[0077] The processing module is used to execute the adaptive high dynamic range measurement algorithm based on time domain stacking described in the above technical solution to calculate and obtain high dynamic range three-dimensional shape data of the measurement scene.

[0078] Preferably, the processing module includes:

[0079] Data pre-processing unit, used to calculate the average white field map and the proportional coefficient of modulation and intensity;

[0080] A parameter calculation unit, used to calculate the optimal parameters according to the set expected accuracy and the inherent parameters of the measurement system;

[0081] The reconstruction unit is used to perform three-dimensional reconstruction of the measurement scene according to the optimal parameters.

[0082] This embodiment further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the computer program implements an adaptive high dynamic range measurement algorithm based on time domain stacking.

[0083] Example 2

[0084] This embodiment provides a specific implementation of an adaptive high dynamic range measurement algorithm based on time domain stacking, such as Figure 1 As shown, the following steps are included:

[0085] S1: Shoot a set of phase-shift fringe patterns with arbitrary exposure time for the measurement scene, calculate the average white field pattern, and calculate the proportional coefficient between modulation and intensity;

[0086] Step S1 includes: shooting a set of phase-shift fringe patterns for an HDR scene to be measured with an arbitrary exposure time, and calculating the intensity of the average white field pattern of the measured scene by the following formula:

[0087]

[0088] Among them I w (x,y) represents the intensity of the pixel coordinate point (x,y) in the average white field image, I n (x,y) represents the intensity of the pixel coordinate point (x,y) at the nth phase shift, n represents the number of stacking times in the time domain stacking method, and N represents the number of phase shift steps in the phase shift method;

[0089] The modulation degree of the fringe pattern is obtained by the following formula:

[0090]

[0091] Where B(x,y) represents the modulation degree B of the pixel coordinate point (x,y); and the modulation degree B(x,y) of the fringe image and the intensity I of the average white field image are obtained. w The proportional coefficient i of (x,y) is

[0092]

[0093] Specifically, a set of three-step phase shift images with 64 frequencies is captured using an exposure time of t = 40000ms, such as Figure 2 As shown, and obtained as Figure 3 The average white field image is shown. The proportionality coefficient i is then calculated based on the intensity of the average white field image and the modulation degree of the fringe pattern.

[0094] In step S1, by calculating the modulation, intensity, and the proportional coefficient of the average white field image, the scene's brightness distribution can be accurately characterized under different exposure conditions. This provides precise baseline data for subsequent adaptive parameter selection, avoiding subsequent measurement errors caused by inaccurate initial data. It also provides reliable basic data for subsequent high-precision 3D reconstruction, ensuring the accuracy, stability, and efficiency of the entire measurement process.

[0095] S2: Draw a grayscale distribution map based on the white field map, and select the area with the minimum grayscale value distribution as the region of interest (ROI)

[0096] In high dynamic range scenes, it is usually difficult to capture details in low reflectivity areas, but by focusing on these areas and performing special processing, the measurement accuracy of these areas can be significantly improved. Therefore, by selecting the area with the minimum grayscale value distribution (the lowest reflectivity area) as the region of interest (ROI), it is possible to focus on processing the areas most prone to measurement errors. Specifically, a grayscale distribution histogram is drawn for the above HDR scene with the x-axis being the grayscale value size (DN) and the y-axis being the number of grayscale value intensity points, as shown in the figure. Figure 4 As shown in the figure, the number of points in each gray value cluster is counted, the gray value size is divided into four categories, and the gray value minimum distribution area, that is, the lowest gray value cluster, is set as the region of interest (ROI), as shown in the figure. Figure 4 As shown. Then, the scale factor of the region of interest (ROI) is selected as the final scale factor i. In fringe projection technology, the lowest gray value cluster usually refers to the darkest part of the image. These parts correspond to the lowest reflectivity areas on the object surface because they reflect the least light onto the camera sensor. Therefore, the lowest gray value cluster is directly related to the lowest reflectivity area on the object surface, reflecting the object's weak ability to reflect light. All subsequent operations are based on the region of interest (ROI). When the phase accuracy of the lowest gray value cluster (ROI) is improved to the desired accuracy through time domain stacking, the phase accuracy of the remaining high reflectivity areas can also be guaranteed. Concentrating resources and computing power on the most challenging areas with the lowest gray values ​​can also optimize the use of measurement resources, reduce unnecessary measurement complexity, and improve the efficiency and effectiveness of the overall system.

[0097] S3: Set the expected accuracy for the region of interest, obtain the inherent parameters of the measurement system, substitute them into the parameter selection formula, and use the adaptive parameter selection method to select the exposure time and number of stacking times.

[0098] Step S3 includes: first setting the phase shift step number N, then finding the intrinsic parameters of the camera used, substituting the intrinsic coefficients into the adaptive parameter selection formula below, and calculating the appropriate stacking number n and the optimal fringe projection intensity I under the measurement scenario. opt .

[0099]

[0100] Among them, I opt is the intensity of the optimal fringe projection (DN), K is the gain parameter of the CCD camera, C is the intrinsic parameter related to the internal noise of the camera, n is the number of stacking times in the time domain stacking method, and N is the number of phase shift steps in the phase shifting method. Indicates the expected accuracy.

[0101] After obtaining the optimal fringe projection intensity, the optimal exposure time is obtained by substituting the pre-taken phase shift image intensity, exposure time, and the optimal fringe projection intensity into the following formula.

[0102]

[0103] Among them, I w , t w Represent the intensity of the pre-shot fringe pattern and the exposure time used for shooting, I opt , t opt They represent the optimal fringe projection intensity and the optimal exposure time, respectively, which are also the fringe intensity and exposure time that meet the expected accuracy.

[0104] Specifically, we select a phase shift step number of N = 3. By searching for the camera's intrinsic parameters, we obtain K = 0.0075 and C = 0.1. Then, we substitute these coefficients into the adaptive parameter selection formula to calculate the appropriate stacking number n = 7 and fringe projection intensity I = 50DN for this scenario. We then substitute the previously captured phase shift image intensity and exposure time, along with the optimal fringe projection intensity, to determine the optimal exposure time of 3.5ms.

[0105] Step S3 uses an adaptive parameter selection method, combining the characteristics of the region of interest with the inherent parameters of the measurement system, to precisely control measurement accuracy and dynamically adjust the exposure time and number of stacking times based on the specific scenario. This not only improves the adaptability of the measurement system and the stability of the measurement results, but also reduces the resource consumption during measurement time, enhances the robustness and consistency of the system in complex measurement scenarios, and ultimately ensures the efficiency and accuracy of high dynamic range measurement tasks.

[0106] S4: Reconstruct the scene using the optimal parameters to obtain actual measurement results with the same accuracy as the expected accuracy.

[0107] Specifically, the optimal exposure time and the optimal number of stacking are selected according to the adaptive parameters to reconstruct the entire scene, and the original scene is stacked as follows Figure 5 The final reconstruction result is shown in Figure 6 As shown in (a).

[0108] The stacking method is:

[0109] After shooting the deformed fringes with a camera, the deformed fringes can be expressed as:

[0110] I n (x,y)=A(x,y)+B(x,y)cos[Φ(x,y)-2πn / N]

[0111] Where n represents the phase shift step, (x, y) represents the pixel coordinates of the point, Φ represents the phase value to be calculated, A and B represent the intensity and modulation of the fringe background, respectively. The phase shift algorithm can be used to obtain the value of Φ:

[0112]

[0113] By introducing the time domain superposition method, the intensity of the pixel point at the same phase shift is superimposed each time the measurement is made. The expression for the intensity of the point can be obtained as follows:

[0114]

[0115] Among them I kn Indicates the intensity value of the point at the kth measurement and the nth step phase shift. Similarly, formula (2) is updated as:

[0116]

[0117] By using this stacking method, the Gaussian noise of the image can be suppressed, thereby significantly improving the signal-to-noise ratio of the image. In addition, in the three-frequency phase unwrapping method, the low-frequency and medium-frequency parts only guide the phase unwrapping, and the high-frequency part is used to obtain fine three-dimensional morphological information. Therefore, the low-frequency and medium-frequency parts only need to be shot once. In the final phase unwrapping link, the phase can be normally unfolded by multiplying n times, such as Figure 7 shown.

[0118] At the same time, two situations that would occur when there is no adaptive parameter selection method are simulated: First, the reconstruction result that would occur when the exposure time is too short is simulated: one of the five groups of phase-shifted fringes is selected for reconstruction, and the reconstruction result is as follows: Figure 6 (b) shown.

[0119] Specifically, in the reconstruction results, while the normal dynamic range ensured successful reconstruction of the entire 3D topography, the phase accuracy of the region of interest (ROI) fell short of expectations, resulting in poor reconstruction results. This was evident in the resulting cross-sectional image, which differed significantly from the results obtained using the adaptive parameter selection method. This reconstruction was deemed unsatisfactory for measurement purposes.

[0120] We then simulated the reconstruction results that would occur if the exposure time was too long. Based on the phase accuracy selection formula, we selected a phase shift map with 7 stacks of 3.5ms exposure time. According to the phase accuracy calculation method, the reconstruction result with a single 24ms exposure time should have the same phase accuracy. Therefore, this example measured the scene with a 24ms exposure time, and the measurement results are as follows: Figure 6 (c) shown.

[0121] Specifically, it can be seen from the reconstruction results that the phase accuracy in the low reflectivity area has reached the expected accuracy, so the detail reconstruction results of the portable hard disk are good, and its profile fluctuations are consistent with the Figure 6 (a) is similar, but due to the long exposure time, both the puppy doll and the white standard plate appear overexposed, which is caused by insufficient dynamic range.

[0122] Comparing the results of the three sets of experiments, we can draw the following conclusions: This method overcomes the drawback of traditional HDR methods, which are mostly based on qualitative judgment of measurement parameters and cannot quantitatively determine the final measurement parameters. Based on the time domain stacking method, through an adaptive parameter selection formula, it increases the measurement dynamic range while obtaining the phase accuracy that can be achieved in the final measurement. This method avoids the problems of overexposure of the scene due to long exposure time and insufficient final measurement accuracy due to insufficient exposure time when measuring HDR scenes. Compared with other methods, this method has the following advantages:

[0123] Compared with other HDR methods, this method can predict the achievable phase accuracy level before measurement, which can maximize the use of time and space costs, and at the same time make quantitative predictions of the measurement results. In certain specific scenarios, it will not cause waste of time and accuracy, and is of guiding significance.

[0124] Compared to similar HDR methods like multi-exposure, this method adaptively selects the optimal exposure time and number of stacks, eliminating the need for empirical analysis or multiple trials. It also utilizes fringe multiplexing to unwrap the phase, significantly reducing the time cost of other HDR methods.

[0125] Example 3

[0126] As an optimization of Example 2, this example is a verification test for the phase accuracy of adaptive parameter selection, using a three-frequency phase unwrapping method combined with a three-step phase shifting technique to reconstruct the unwrapped phase of the standard plane. Figure 8 shown.

[0127] In order to verify the correctness of the theoretical formula, the phase of the standard ceramic plane was reconstructed, and the average of 300 measurement results was used as the true value. The theoretical phase error obtained by the theoretical formula was compared with the actual phase error obtained by actual measurement. First, the effect of exposure time on the phase error was compared. The difference between the theoretical error under different stacking times and the measured result and the true value was used as the phase error for comparison. The results are shown in the figure below. Figure 9 The difference between theoretical error and actual error under different exposure times is compared, as shown in Figure 10 As shown, the comparison method is the same as above.

[0128] Specifically, the magnitude and trend of the theoretical and actual values ​​based on the number and exposure time curves are generally consistent, with the maximum difference less than 0.0002 rad, which can be attributed to systematic error, thus proving the correctness of the theoretical formula. This experiment shows that the theoretically predicted phase error can be used directly to replace the experimental phase error, which provides guidance for predicting measurement accuracy. Furthermore, this set of experiments also demonstrates that when the relevant parameters of the measurement system are known in advance, the proposed method can be used to predict the phase accuracy that the system can measure, eliminating the need for empirical judgment.

[0129] Example 4

[0130] This embodiment provides an adaptive high dynamic range measurement device based on time domain stacking, which is suitable for three-dimensional shape measurement of high dynamic range scenes in industrial and scientific research fields.

[0131] 1. The device includes the following modules:

[0132] Projection module: Used to project a phase-shifted fringe pattern onto the measurement scene. This module typically includes a high-precision digital projector that can generate sinusoidal fringe patterns with different frequencies and phase shifts and project them onto the measurement scene.

[0133] Image acquisition module: This module is used to capture phase-shifted fringe images of the measurement scene at arbitrary exposure times. This module typically consists of a high dynamic range (HDR) camera that can adjust the exposure time based on the scene's reflectivity to capture the details of the fringe pattern.

[0134] Processing module: used to execute the adaptive high dynamic range measurement algorithm based on time domain stacking. The processing module includes the following submodules:

[0135] (1) Data preprocessing unit: used to calculate the average white field map and the ratio coefficient of modulation and intensity. This unit receives image data from the image acquisition module and performs the preprocessing steps of the algorithm.

[0136] (2) Parameter calculation unit: This unit calculates the optimal parameters based on the desired accuracy and the inherent parameters of the measurement system. Through an adaptive parameter selection formula, the parameter calculation unit can dynamically adjust the exposure time and number of stacking times to optimize the measurement results.

[0137] (3) Reconstruction unit: used to perform three-dimensional reconstruction of the measurement scene based on the optimal parameters. This unit uses phase shift profilometry and phase unwrapping methods to generate high dynamic range three-dimensional topography data of the measurement scene.

[0138] 2. Workflow

[0139] First, the projection module projects a fringe pattern onto the surface of the object to be measured. The image acquisition module captures the deformed fringe images at different exposure times. The data preprocessing unit receives these images and calculates the average white field image and the modulation-intensity ratio. The parameter calculation unit uses these results, combined with the desired accuracy and the inherent parameters of the measurement system, to determine the optimal exposure time and number of stacking steps. Finally, the reconstruction unit performs 3D reconstruction of the measured data based on the calculated optimal parameters, generating a high-dynamic-range 3D topography of the scene.

[0140] Example 5

[0141] This embodiment provides a computer-readable storage medium storing a computer program. When the program is executed by a processor, it can implement an adaptive high dynamic range measurement algorithm based on time domain stacking.

[0142] 1. Computer-readable storage medium

[0143] The storage medium can be a hard disk, a solid-state drive (SSD), an optical disk, a flash drive, or any other device capable of storing digital information. The computer program stored in the storage medium can be executed in a standard computer, an embedded system, or a dedicated measurement device.

[0144] 2. Program Functions

[0145] Data input module: This module receives phase-shift fringe image data from the measuring device and performs necessary preprocessing on it.

[0146] Parameter calculation module: This module implements the adaptive parameter selection formula and calculates the optimal exposure time and number of stacking times through the input measurement data and system parameters.

[0147] 3D reconstruction module: This module performs phase unwrapping algorithm and phase shift profilometry to reconstruct the processed fringe image data into a high dynamic range 3D topography image.

[0148] Result output module: This module is responsible for outputting the final 3D reconstruction results. The results can be saved to a storage medium or output to other analysis tools through an interface.

[0149] 3. Usage scenarios

[0150] When loaded onto a measurement device or computing equipment and executed by a processor, the computer program automates the entire measurement process, from data acquisition to final 3D reconstruction. This program significantly improves measurement accuracy in high dynamic range scenarios and reduces the time and resources required for measurement.

[0151] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An adaptive high dynamic range measurement method based on time domain stacking, characterized in that: The following steps are involved: S1. Obtain a phase-shift fringe pattern of a measurement scene and obtain an average white field pattern of the measurement scene; S2. selecting a region of interest of the measurement scene according to the average white field image; S3. Setting a desired accuracy for the region of interest, and obtaining optimal parameters for measuring the region of interest through an adaptive parameter selection method; The adaptive parameter selection method further includes obtaining the inherent parameters of the measurement system and substituting the inherent parameters into the parameter selection formula as follows: Obtain the optimal fringe projection intensity under the measurement scenario ; Where K is the gain parameter of the camera, C is the intrinsic parameter related to the internal noise of the camera, is the desired accuracy of the setting, represents the proportional coefficient obtained from the modulation and intensity of the average white field image, m represents the number of stacking times in the time domain stacking method, and N represents the number of phase shift steps in the phase shift method; The optimal fringe projection intensity Substitute into the following formula to find the optimal exposure time : in, , Respectively represent the intensity of the pre-shot fringe pattern and the exposure time used for shooting; the optimal parameters include the optimal fringe pattern projection intensity and the optimal exposure time; S4. Reconstruct the measurement scene according to the optimal parameters to obtain an actual measurement result with the same accuracy as the expected accuracy.

2. The adaptive high dynamic range measurement method based on time domain stacking according to claim 1, characterized in that: Step S1 includes capturing a set of phase-shift fringe patterns at different exposure times for an HDR scene to be measured, and obtaining an average white field pattern according to the following formula: ， in, Indicates the pixel coordinate point in the white field image The strength of Indicates the pixel coordinate point of the nth phase shift strength.

3. The adaptive high dynamic range measurement method based on time domain stacking according to claim 2, characterized in that: Step S1 includes obtaining the modulation index, in, Represents pixel coordinate points Modulation degree, proportional coefficient .

4. The adaptive high dynamic range measurement method based on time domain stacking according to claim 3, characterized in that: Step S2 of selecting the region of interest involves drawing a grayscale histogram of the average white field image and selecting the cluster with the lowest grayscale value as the region of interest.

5. The adaptive high dynamic range measurement method based on time domain stacking according to claim 4, characterized in that: In step S4, the measurement scene is reconstructed using the optimal parameters by adopting phase shift profilometry, and a phase unwrapping method is used to perform three-dimensional topography reconstruction.

6. The adaptive high dynamic range measurement method based on time domain stacking according to claim 5, characterized in that: The phase unwrapping method is a three-frequency phase unwrapping method, in which the low-frequency and medium-frequency parts are used to guide the phase unwrapping, and the high-frequency part is used to obtain fine three-dimensional morphology information.

7. An adaptive high dynamic range measurement device based on time domain stacking, characterized in that: include: A projection module, used for projecting a phase-shifted fringe pattern onto a measurement scene; An image acquisition module, configured to acquire a phase-shifted fringe image of the measurement scene at any exposure time; A processing module is used to execute the measurement method according to any one of claims 1 to 6, and calculate and obtain high dynamic range three-dimensional shape data of the measurement scene.

8. The adaptive high dynamic range measurement device based on time domain stacking according to claim 7, characterized in that: The processing module includes: Data pre-processing unit, used to calculate the average white field map and the proportional coefficient of modulation and intensity; A parameter calculation unit, used to calculate the optimal parameters according to the set expected accuracy and the inherent parameters of the measurement system; The reconstruction unit is used to perform three-dimensional reconstruction of the measurement scene according to the optimal parameters. 9 . A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the computer program implements the adaptive high dynamic range measurement method based on time domain stacking according to any one of claims 1 to 6 .

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