A multi-frame averaging shearing speckle interferometry method

Abstract

The present invention discloses a multi-frame averaging shearing speckle interferometry method, which is applied to a multi-frame averaging shearing speckle interferometry system. The system includes a multi-frame image acquisition device based on hardware synchronization control, a phase recovery device, and a single 3×3 sine-cosine mean filter device. The present invention uses a high-frequency synchronous driver to generate multi-step staircase signals and high-frequency square wave signals to synchronously control the camera and PZT to achieve millisecond-level synchronization. This method can accurately capture multi-frame phase shift images under a fixed phase shift state, meeting dynamic measurement requirements. Based on the multi-frame averaging pre-phase recovery method and the multi-frame averaging post-phase recovery method, a single 3×3 sine-cosine mean filter is combined to significantly reduce phase noise while retaining high-frequency details. The multi-frame averaging pre-phase recovery method and the multi-frame averaging post-phase recovery method are used to balance image accuracy and calculation speed. While maintaining high accuracy, the calculation time is only 1.2 times that of traditional methods.

Description

Technical Field

[0001] The present invention relates to the field of optical detection technology, and in particular to a multi-frame averaging shearing speckle interferometry method. Background Art

[0002] Composite materials are widely used in high-performance applications such as aerospace, where internal defects can severely compromise structural integrity and service life, posing a significant safety hazard. Rapid, accurate, and non-destructive testing methods are crucial to mitigating safety risks and reducing failure rates.

[0003] Shearing speckle interferometry (SSI) is a full-field, non-contact, non-destructive optical inspection method based on laser interferometry. It is designed to detect minute surface deformations and has been widely used for defect detection in aerospace-grade composite materials. It can identify subsurface defects in components such as control surfaces and fuselage structures, effectively detecting defects such as delamination, debonding, poor adhesion, wrinkles, cracks, and impact damage.

[0004] In traditional speckle interferometry, sheared speckle phase images typically contain a significant amount of random noise, primarily arising from the inherent randomness of the speckle pattern and various interference factors during the image acquisition process. When coherent laser light illuminates a rough surface, it produces randomly distributed speckles with random intensity and phase, resulting in multiplicative noise. Other noise sources include optical component imperfections, ambient light fluctuations, dark current, and detector-related quantum noise. Furthermore, limitations of image processing algorithms and insufficient spatial sampling can introduce post-acquisition artifacts, which together degrade fringe quality and measurement accuracy.

[0005] Traditional speckle interferometry image denoising methods include adaptive filtering methods using anisotropic sine-cosine averaging, complex amplitude domain filtering methods running in the complex amplitude reconstruction stage, methods combining Fourier transform-based phase filtering with clustering algorithms, and methods based on wavelet transforms. Modern speckle interferometry image denoising methods are deep learning methods using the Speckle Denoising Convolutional Neural Network (SDCNN).

[0006] In the process of implementing the technical solution of the present invention, the inventor of this patent discovered at least the following technical problems in the prior art:

[0007] 1. Conflict between noise suppression and detail preservation: Traditional filtering algorithms may introduce new noise or artifacts into images, especially when filter parameters are poorly selected. The filtering process can also reduce image resolution, particularly when applying certain global filters. This is because these filters typically alter the frequency content of the image, resulting in a loss of high-frequency detail and impacting micro-defect detection accuracy.

[0008] 2. Dynamic measurement limitations: Existing methods rely on single-frame or small-frame image acquisition, which makes it difficult to balance noise suppression and temporal resolution. In addition, hardware asynchronous operation easily introduces phase offset errors.

[0009] 3. High computational complexity: Multi-stage filtering or deep learning algorithms require complex computing resources and are difficult to meet real-time detection requirements. For example, convolutional neural networks (CNNs) require a large amount of training data and are computationally expensive.

[0010] In summary, the existing speckle interferometry image denoising methods cannot simultaneously take into account image accuracy, computational efficiency and dynamic measurement requirements. Summary of the Invention

[0011] The present invention provides a multi-frame averaging shearing speckle interferometry method for high-precision detection of surface micro-deformations and internal defects in composite materials. This method solves the problem that existing speckle interferometry image denoising methods cannot simultaneously take into account image accuracy, computational efficiency, and dynamic measurement requirements. It can meet the requirements of the aviation and aerospace fields for noise suppression and detail preservation of high-resolution phase images, while also taking into account high precision, high efficiency, and dynamic adaptability.

[0012] The present invention provides a multi-frame averaging shearing speckle interferometry method, which is applied to a multi-frame averaging shearing speckle interferometry system. The system includes an image acquisition device based on hardware synchronization control, a phase recovery device, and a single 3×3 sine-cosine mean filtering device. The method includes:

[0013] In a multi-frame image acquisition device based on hardware synchronous control, a high-frequency synchronous driver generates a multi-step staircase signal and a high-frequency square wave signal to synchronously control the camera and PZT. The multi-step staircase signal serves as the PZT drive signal to control the PZT displacement, and the square wave signal serves as the camera trigger signal to trigger the camera to capture images through the rising edge. The rising edge or falling edge of each step of the staircase signal is strictly synchronized with the rising edge of the square wave, and multiple camera frame periods correspond to the duration of one step signal. A multi-frame multi-step time phase shift method is used to introduce multiple discrete phase shifts into the PZT. Before and after deformation of the measured object, the camera captures multi-frame phase shift images of the PZT in multiple phase shift intensity modes at the same position, obtaining multi-frame phase shift images in the reference state and multi-frame phase shift images in the deformed state.

[0014] In the phase recovery device, a multi-frame average pre-phase recovery method or a multi-frame average post-phase recovery method is used to process the multi-frame phase shift image in the reference state and the multi-frame phase shift image in the deformed state to obtain a phase reconstruction image. The multi-frame average pre-phase recovery method calculates the average value of the multi-frame phase shift images in the two states before phase recovery to obtain an average phase shift image, and then calculates an average phase difference image as the phase reconstruction image. The multi-frame average post-phase recovery method averages multiple phase differences in the multi-frame phase shift images in the two states to obtain a final phase difference image as the phase reconstruction image.

[0015] The single 3×3 sine-cosine mean filtering device is used to perform a single 3×3 sine-cosine mean filtering process on the phase reconstruction image to obtain a filtered image.

[0016] The one or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0017] 1. Noise suppression and detail preservation: Based on the multi-frame averaging pre-phase recovery method and the multi-frame averaging post-phase recovery method, combined with a single 3×3 sine-cosine mean filtering process, multi-frame averaging combined with single filtering significantly reduces phase noise while preserving high-frequency details.

[0018] 2. Dynamic adaptability: A high-frequency synchronous driver generates multi-step staircase signals and high-frequency square wave signals to synchronously control the camera and PZT to achieve millisecond-level synchronization. This enables accurate acquisition of multi-frame phase shift images under a fixed phase shift state, supports high-speed multi-frame acquisition, and meets dynamic measurement requirements.

[0019] 3. Computational efficiency optimization: Two multi-frame average phase retrieval methods (multi-frame average pre-phase retrieval method and multi-frame average post-phase retrieval method) balance image accuracy and computational speed. The multi-frame average post-phase retrieval method maintains high accuracy while taking only 1.2 times the computation time of traditional methods.

[0020] 4. Multiple applications: In addition to being applicable to shear speckle interferometry, the present invention can also be applied to electronic speckle interferometry, laser speckle interferometry, and laser holographic imaging technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the shear speckle interferometry process using a four-frame four-step temporal phase shifting method;

[0022] Figure 2 This is a diagram of the process of obtaining phase shift images using the four-frame four-step time phase shift method under high-frequency synchronous triggering;

[0023] Figure 3 Phase reconstruction images obtained using the multi-frame average pre-phase recovery method and the multi-frame average post-phase recovery method;

[0024] Figure 4 For Figure 3 The filtered image obtained by applying a single 3×3 sine-cosine mean filter to the phase reconstructed image in;

[0025] Figure 5 for Figure 3 Phase reconstruction images in Figure 4 The denoising performance evaluation index graph corresponding to the filtered image in ;

[0026] Figure 6 Schematic diagram of the calculation time (normalized time) of the multi-frame averaging pre-phase recovery method and the multi-frame averaging post-phase recovery method;

[0027] Figure 7 Phase reconstruction images obtained using the multi-frame average pre-phase recovery method and the multi-frame average post-phase recovery method in non-destructive testing experiments;

[0028] Figure 8 For Figure 7 The filtered image obtained by applying a single sine-cosine mean filter to the phase reconstruction image in;

[0029] Figure 9 for Figure 7 Phase reconstruction images in Figure 8 The denoising performance evaluation index diagram corresponding to the filtered image in;

[0030] Figure 10 Schematic diagram of the shearing speckle interferometry system based on hardware synchronized multi-frame averaging of the present invention;

[0031] In the figure: Method 1 - multi-frame averaging pre-phase recovery method; Method 2 - multi-frame averaging post-phase recovery method. DETAILED DESCRIPTION

[0032] The present invention provides a multi-frame averaging shearing speckle interferometry method for high-precision detection of surface micro-deformations and internal defects in composite materials. This method solves the problem that existing speckle interferometry image denoising methods cannot simultaneously take into account image accuracy, computational efficiency, and dynamic measurement requirements. It can meet the requirements of the aviation and aerospace fields for noise suppression and detail preservation of high-resolution phase images, while also taking into account high precision, high efficiency, and dynamic adaptability.

[0033] First, the terms that appear in the specification will be explained respectively.

[0034] Multi-frame averaging: By collecting multiple speckle interferometry images under a fixed phase shift state, they are used as multi-frame phase shift maps, and the multi-frame phase shift maps are averaged to improve the imaging quality.

[0035] Phase recovery: The process of extracting phase information from a speckle interferometer pattern to calculate the displacement change of the object surface.

[0036] PZT: Includes piezoelectric materials and mirrors that produce micron-level displacement through voltage control, used to precisely adjust the phase of the optical path.

[0037] Secondly, the core innovations of the present invention mainly include the following three points.

[0038] 1. In the multi-frame image acquisition device based on hardware synchronous control, a high-frequency synchronous driver generates a multi-step staircase signal and a high-frequency square wave signal to synchronously trigger the PZT and the camera. Based on the multi-step time phase shift method, a multi-frame multi-step time phase shift method is improved to obtain a multi-frame phase shift image accurately under a fixed phase shift state.

[0039] 2. The multi-frame averaging pre-phase recovery method and the multi-frame averaging post-phase recovery method are proposed for the first time. These two multi-frame averaging phase recovery methods can significantly improve imaging quality and enhance noise suppression capabilities while preserving critical phase detail information. Specifically, by increasing the number of frames, phase fringe noise is significantly reduced without compromising image resolution. Experimental verification shows that the equivalent number of fixations (ENL) of the phase reconstructed image is enhanced and the speckle suppression index (SSI) and speckle mean preservation index (SMPI) values are reduced after using the multi-frame averaging phase recovery method, indicating that imaging quality improves with increasing the number of frames.

[0040] Among them, the multi-frame average pre-phase recovery method reduces the amount of calculation by directly averaging the multi-frame phase shift map and then calculating the phase difference, which is suitable for real-time detection scenarios;

[0041] The multi-frame averaging phase recovery method improves accuracy by independently calculating the phase shift map of each frame and then averaging it. It is suitable for static high-precision detection scenarios.

[0042] 3. Single 3×3 sine-cosine mean filtering: After multi-frame average phase recovery, the phase reconstruction image is obtained and a single 3×3 sine-cosine mean filtering is performed on it to avoid the loss of high-frequency details caused by multi-stage filtering.

[0043] To better understand the multi-frame averaging shearing speckle interferometry method of the present invention, the following detailed description is provided in conjunction with the accompanying drawings and specific embodiments. Obviously, the embodiments described herein are only a subset, not all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments herein without inventive effort are considered within the scope of protection of the present invention.

[0044] like Figure 10 As shown, the present invention provides a multi-frame averaging shearing speckle interferometry method, which is applied to a multi-frame averaging shearing speckle interferometry system. The system primarily comprises three components: a multi-frame image acquisition device based on hardware synchronization control, a phase recovery device, and a single 3×3 sine-cosine mean filter. The present invention's improvements focus on the first two components.

[0045] The multi-frame image acquisition device based on hardware synchronous control of the present invention is a Michelson-type misalignment interferometer device, which is composed of a laser, a Michelson-type misalignment interferometer, a camera, a shear mirror and a piezoelectric driven mirror (PZT-basedMirror, referred to as PZT). The laser generates a coherent light beam. The Michelson-type misalignment interferometer splits the incident light beam into two beams. After passing through the object to be measured, the two light beams will be slightly misaligned. The camera captures the two misaligned images generated by the Michelson-type misalignment interferometer and records the interference fringe pattern. The shear mirror in the Michelson-type misalignment interferometer introduces misalignment of the light beam. Therefore, it produces interference of two coherent light beams. The PZT is used to precisely control the amount of misalignment. The degree of misalignment can be adjusted by adjusting the position of the mirror in the PZT.

[0046] In speckle interferometry, the highly accurate and robust TPS (Time Phase Shifting) method is used to measure light interference. TPS methods can be further categorized into single-step, three-step, four-step, and five-step methods. Non-single-step TPS methods with three or more steps are called multi-step TPS methods. The following uses the four-step TPS method as an example.

[0047] like Figure 1 As shown in the figure, the laser beam emitted by the laser passes through the expander and illuminates the surface of the measurement object. The laser light is diffusely reflected on the surface of the measurement object, and the resulting beam is split into two misaligned beams by the Michelson-type misalignment interferometer. These two misaligned beams are reflected by mirrors 1 and 2, respectively. Subsequently, they interfere with each other to form speckle interference fringes recorded by the camera. The camera captures four phase shift images in the reference state and four phase shift images in the deformed state. According to the phase in the reference state, the phase shift of the image is converted into the image of the image of the deformation state. Phase with deformation , calculate the phase difference .

[0048] During measurement, the PZT is driven by a high-frequency synchronous driver, using a four-step TPS method to introduce four discrete phase shifts. At each discrete phase shift, the camera captures a phase shift image of the speckle interference pattern at each phase shift intensity pattern. Four phase shift images are captured for each state, before and after deformation, for each of the four phase shift intensity patterns.

[0049] First, obtain four phase shift images of the measurement object in the reference state ,in i represents the phase shift intensity pattern. The phase shift between adjacent patterns is , which is a fixed phase shift. Each phase shift diagram under the reference state represents:

[0050] ,

[0051] Where, represents the four phase shift maps obtained in the reference state, represents the image pixel coordinates, is the pixel coordinate in the speckle interferogram The background image at is the pixel coordinate The modulation image of the interference term at is the pixel coordinate in the reference state Phase image at .

[0052] Secondly, similar to the previous step, obtain four phase shift images of the measured object in the deformed state .in i represents the phase shift intensity pattern, and the phase shift between adjacent modes is , which belongs to fixed phase shift. Each phase shift diagram under deformation state is expressed as:

[0053] ,

[0054] Where, represents the four phase shift images obtained in the deformed state, is the pixel coordinate in the deformed state Phase image at .

[0055] In order to avoid repeatedly performing time-consuming inverse tangent operations, the following phase difference calculation formula is used to calculate the phase difference at different pixel coordinates: , the phase difference calculation formula is expressed as:

[0056] ,

[0057] Where, Represents pixel coordinates The displacement in the vertical direction, Represents pixel coordinates The amount of displacement in the horizontal direction.

[0058] To achieve multi-frame averaging, a multi-frame multi-step TPS method was designed by adding multi-frame processing features to the traditional multi-step TPS method. Secondly, a high-frequency synchronous driver component for hardware synchronization control was added to the traditional speckle interferometry image acquisition device, resulting in a multi-frame image acquisition device based on hardware synchronization control.

[0059] The high-frequency synchronous driver controls the camera and PZT simultaneously. The following is an example of the four-frame four-step TPS method. Figure 2 The high-frequency synchronous driver simultaneously generates a high-frequency square wave signal and a four-step staircase signal. The rising or falling edge of each step of the four-step staircase signal is strictly synchronized with the rising edge of the square wave. Furthermore, the four-step staircase signal drives the PZT to generate accurate interference fringes, while the rising edge of the square wave triggers the camera to capture the image.

[0060] A step signal in a multi-step staircase signal corresponds to multiple square wave signals, that is, the camera frame period is a small fraction of the duration of a step signal. According to this hardware synchronization control method, it is possible to accurately capture multiple frames of phase shift images corresponding to multiple phase shift intensity patterns of PZT at the same position. The multi-frame phase shift image under the reference state is recorded as ,in i represents the phase shift intensity pattern, k Indicates the number of frames. Correspondingly, the multi-frame phase shift diagram under the deformation state is recorded as .exist Figure 2 In the example, one step signal corresponds to three square wave signals ( n =3), represents the camera frame period, Represents the duration of a step signal, and thus the camera frame period Is a step signal 1 / 3 of the duration , the solid dots represent the moments when the phase-shifted image is fully captured, and λ represents the wavelength of the laser.

[0061] The high-frequency synchronization of the PZT and the camera ensures that multiple frames of phase shift images can be accurately acquired at each phase shift intensity in both the reference and deformed states, thereby being used to perform the subsequent phase recovery step.

[0062] In the phase recovery device, the present invention improves the conventional phase recovery algorithm and proposes two improved phase recovery methods, namely a multi-frame average pre-phase recovery method and a multi-frame average post-phase recovery method.

[0063] The first one: multi-frame average pre-phase recovery method;

[0064] The multi-frame average pre-phase recovery method is characterized by obtaining the average value of multi-frame phase shift images before phase recovery. This method calculates the average value of multi-frame phase shift images in two states before phase recovery to obtain an average phase shift image. This average phase difference image, which serves as the phase reconstruction image, is calculated based on the displacement change of the measured object.

[0065] The multi-frame average pre-phase recovery method specifically includes the following steps: first, calculating the average value of multiple frames of phase shift images generated in each phase shift intensity mode under the reference state to obtain multiple average phase shift images under the reference state; second, calculating the average value of multiple frames of phase shift images generated in each phase shift intensity mode under the deformed state to obtain multiple average phase shift images under the deformed state; finally, based on the multiple average phase shift images under the reference state and the multiple average phase shift images under the deformed state, calculating the average phase difference image caused by the displacement change of the measured object.

[0066] Taking the multi-frame four-step TPS method as an example, first, the average values of the multi-frame phase shift images generated by the four phase shift intensity modes under the reference state are calculated to obtain the four average phase shift images under the reference state. , the calculation formula is:

[0067] ,

[0068] in, k Indicates the number of frames, n Indicates the maximum number of frames. Multi-frame phase shift diagrams generated under four phase shift intensity modes when representing the reference state.

[0069] Then, the average values of the multi-frame phase shift images generated by the four phase shift intensity modes under the deformation state are calculated to obtain four average phase shift images under the deformation state. , the calculation formula is:

[0070] ,

[0071] in, Represents the multi-frame phase shift images generated under four phase shift intensity modes in the deformed state.

[0072] Finally, based on the four average phase shift images under the reference state , Four average phase shift images under deformation state , calculate the average phase difference image caused by the displacement change of the measurement object , the calculation formula is:

[0073] ,

[0074] in, Represents pixel coordinates The average displacement in the vertical direction, Represents pixel coordinates The average displacement in the horizontal direction.

[0075] The second method: phase recovery method after multi-frame averaging;

[0076] The multi-frame averaging phase retrieval method is an alternative to the multi-frame averaging pre-phase retrieval method. It averages the multiple phase differences of multi-frame phase shift images under two states to obtain the final phase difference image as the phase reconstruction image.

[0077] The multi-frame averaging phase recovery method specifically includes the following steps: first, calculating multiple phase images of the multi-frame phase shift map generated under multiple phase shift intensity modes under the reference state to obtain multiple phase images under the reference state; then, calculating multiple phase images of the multi-frame phase shift map generated under multiple phase shift intensity modes under the deformed state to obtain multiple phase images under the deformed state; next, based on the multiple phase images under the reference state and the multiple phase images under the deformed state, calculating multiple phase difference images generated under multiple phase shift intensity modes; finally, averaging the multiple phase difference images to calculate the final phase difference image.

[0078] Taking the multi-frame four-step TPS method as an example, the multi-frame phase images of the multi-frame phase shift images generated by the four phase shift intensity modes under the reference state are calculated to obtain the multi-frame phase images under the reference state. , the calculation formula is:

[0079] ,

[0080] in, k Indicates the number of frames, n Indicates the maximum number of frames. Multi-frame phase shift diagrams generated under four phase shift intensity modes when representing the reference state;

[0081] Calculate multiple phase images of the multi-frame phase shift images generated by the four phase shift intensity modes under the deformation state to obtain the multi-frame phase image under the deformation state , the calculation formula is:

[0082] ,

[0083] in, Multi-frame phase shift images generated under four phase shift intensity modes when representing the deformation state;

[0084] Multi-frame phase images based on reference state , multi-frame phase images in deformed state , calculate the multi-frame phase difference images generated by four phase shift intensity modes ;

[0085] Multi-frame phase difference images Take the average value and calculate the final phase difference image , the calculation formula is:

[0086] .

[0087] Multi-frame phase difference images There are two corresponding calculation formulas, which can be selected according to actual conditions.

[0088] Multi-frame phase difference images The first calculation formula is:

[0089] .

[0090] Multi-frame phase difference images The second calculation formula is:

[0091] ,

[0092] in, Indicates the average displacement of multiple frames in the vertical direction, Indicates the average displacement of multiple frames in the horizontal direction, The multi-frame phase shift diagram generated in four phase shift intensity modes when representing the reference state, Represents the multi-frame phase shift diagram generated in four phase shift intensity modes in the deformed state, Represents pixel coordinates.

[0093] The second calculation formula improves accuracy by averaging over the phase levels. However, it requires n This increases the computational complexity and reduces the processing speed, making it less suitable for real-time or dynamic measurements. In order to improve computational efficiency, the phase difference image used to calculate the final The formula is simplified, and the calculation formula after simplification is:

[0094] ,

[0095] in, Represents multiple frames of simplified phase difference images.

[0096] Finally, in a single 3×3 sine-cosine mean filtering device, a single 3×3 sine-cosine mean filtering is performed on the phase reconstruction image after the phase recovery processing to obtain a filtered image.

[0097] In order to evaluate the computational efficiency of the two multi-frame average phase recovery methods of the present invention, after obtaining the phase reconstructed image, it also includes: using normalized time to evaluate the computational efficiency of the multi-frame average pre-phase recovery method and the multi-frame average post-phase recovery method.

[0098] In order to evaluate the denoising performance of the multi-frame average shearing speckle interferometry method provided by the present invention, after obtaining the filtered image, the method further includes: ENL、SSI 、 SMPI At least one indicator is used to evaluate the denoising performance of the filtered image.

[0099] The following are the three denoising performance indicators: ENL (equivalent number of fixations), SSI (spot suppression index) and SMPI (Spot Mean Preservation Index).

[0100] ENL Measures the noise smoothing effect in uniform image regions, with higher ENL A value of indicates stronger speckle noise reduction in uniform areas and is calculated as follows:

[0101] ,

[0102] in, represents the denoised phase image, represents the average value of the denoised phase image, Represents the variance of the denoised phase image.

[0103] SSI Noise suppression is quantified by comparing the denoised image with the original speckle image, SSI The lower the value, the stronger the noise suppression ability is. The calculation formula is as follows:

[0104] ,

[0105] in, represents the original speckle image, represents the average speckle intensity in the uniform area of the image, represents the average value of the original speckle image, represents the variance of the original speckle image.

[0106] SMPI Can effectively evaluate noise suppression performance and average hold performance, SMPI The lower the value, the better the denoising method performs in terms of mean preservation and noise reduction. The calculation formula is as follows:

[0107] ,

[0108] Among them, the coefficient .

[0109] Deformation measurement embodiment;

[0110] In order to verify the feasibility of the proposed multi-frame averaging shear speckle interferometry method, a shear mirror, PZT, synchronous driving circuit, camera, spectrometer, micro-deformation device and laser are used as the optical setup of the shear speckle deformation measurement device.

[0111] During implementation, the composite panel was fixed at its four edges to ensure uniform laser irradiation and reduce the influence of ambient noise. Small displacements were manually introduced using micrometer screws. An eight-frame four-step TPS method was used to capture multiple phase-shifted speckle patterns of the composite panel before and after deformation. A custom-designed synchronous drive circuit was used as a high-frequency synchronous drive, and eight phase-shift images were captured at each phase before and after deformation as eight-frame phase-shift images. The eight-frame phase-shift images in the reference state captured before deformation are expressed as , the eight-frame phase shift image captured in the deformed state after deformation is expressed as .

[0112] In order to evaluate the effect of frame number on the quality of phase image, phase recovery was performed using different frame numbers. The multi-frame average pre-phase recovery method was used to obtain Figure 3 The phase reconstruction images shown in A1 to A8 in the figure, where "Am" (m=1-8) represents the phase reconstruction image restored using the m-frame phase shift image in each offset step. Similarly, using the multi-frame averaging and phase recovery method, we obtain Figure 3 The phase reconstruction images shown in B1 to B8, where "Bm" (m=1-8) represents the phase reconstruction image restored using the m-frame phase shift image in each migration step. Since when m=1, the two multi-frame average pre-phase recovery methods are consistent with the traditional four-step TPS method, Figure 3 The phase reconstruction diagram shown in A1 and Figure 3 The phase reconstruction image shown in B1 is the same.

[0113] from Figure 3 It can be seen that both multi-frame average phase recovery methods significantly improve the quality of the phase reconstruction image. However, it is still recommended to use a simple post-processing filter, which can be a single 3×3 sine-cosine mean filter. In this embodiment, a sine-cosine mean filter with a 3×3 window is applied to all reconstructed phase images. After filtering, Figure 3 A1 to A8 in the equation are respectively Figure 4 in to .same, Figure 3 B1 to B8 in the Figure 4 in to .

[0114] In order to further evaluate the denoising performance of the proposed method, we used ENL 、 SSI and SMPI These three denoising performance indicators are Figure 3 and Figure 4 The image quality before and after filtering is evaluated as shown. Note that when the filter exceeds the average value, ENL and SSI No longer reliable. However, SMPI It can effectively evaluate the noise suppression performance and average hold performance.

[0115] calculate Figure 3 and Figure 4 Each subgraph in ENL 、 SSI and SMPI Value, the result is Figure 5 The first value of the indicator is divided by each value of the indicator. SSI and SMPI The values are normalized.

[0116] In addition, this embodiment also evaluates the computational efficiency of two multi-frame average phase recovery methods. The test environment mainly includes a computer with an AMD Ryzen 9 5900HS CPU and 16 GB RAM running Windows 11 system, and MATLAB R2024a software is running on the computer. Figure 6 The time consumption results of the two phase recovery methods proposed in this invention are shown. Figure 6 The evolution of the normalized computation time, defined as the ratio of the execution time of each multi-frame averaged phase retrieval method to the execution time of the traditional single-frame phase retrieval method, is shown.

[0117] Nondestructive testing examples;

[0118] In order to verify the effectiveness of the proposed multi-frame averaged shear speckle interferometry method in practical applications, a non-destructive testing experiment was carried out on a metal-rubber bonded sample. A circular debonding defect was artificially embedded in the sample. A 1kW halogen lamp was used to thermally excite the sample surface for 5 seconds. A shear speckle deformation measurement device was used to capture the resulting speckle image, and two phase multi-frame average phase recovery methods were applied to the phase reconstruction stage. The initial phase reconstruction images obtained by the multi-frame average pre-phase recovery method at different frame numbers are shown in Figure 2. Figure 7 As shown in A1 to A8 in FIG; Under different frame numbers, the initial phase reconstruction image obtained by the multi-frame averaging and phase recovery method is shown in FIG. Figure 7 As shown in B1 to B8.

[0119] right Figure 7 Each phase reconstruction image in the image is processed by a single 3×3 sine-cosine mean filter, and the obtained Figure 8 The filtered images in . Figure 7 A1 to A8 in the equation are obtained as follows Figure 8 in to , Figure 7 B1 to B8 in the Figure 8 in to .

[0120] Similar to the deformation measurement embodiment, the denoising performance evaluation indicators of the two multi-frame average phase recovery methods are ENL 、 SSI and SMPI like Figure 9 shown.

[0121] The multi-frame averaging shear speckle interferometry method of the present invention is tested below in combination with the experimental results in the deformation measurement embodiment and the non-destructive testing embodiment.

[0122] like Figure 3 and Figure 7 As shown in the experimental results, there is an obvious inverse relationship between the frame number and the noise level in the phase reconstruction image. Figure 5 and Figure 9 The denoising performance indicators shown in ( ENL 、 SSI and SMPI ) and confirmed that the increase in the number of frames will lead to ENL Enhancement, SSI and SMPI These results verify the denoising effect of the two multi-frame average phase recovery methods.

[0123] The enhancement of noise suppression is positively correlated with the number of frames used. However, due to hardware limitations, the maximum number of speckle patterns acquired in the present invention under the same phase shift state is limited to 8 frames. Figure 5 The left picture and Figure 9 The trend line on the left of the graph shows that further increase in the number of frames will lead to continuous improvement in noise suppression performance.

[0124] For dynamic measurements, increasing the frame rate is crucial to ensure temporal resolution. Therefore, the optimal frame rate should be determined based on the specific temporal and spatial resolution requirements of the application.

[0125] also, Figure 4 and Figure 8It shows that a single-pass mean filtering operation with a 3×3 window is sufficient to achieve high-quality phase recovery. Traditional methods usually rely on multiple filtering stages or more computationally intensive denoising algorithms, and need to reduce phase image noise by suppressing fine structural details. For example, in defect detection applications, small defects can be filtered out during the filtering process. The multi-frame averaging sheared speckle interferometry method proposed in the present invention only requires one mean filtering operation to achieve similar effects to multiple filtering and complex filtering algorithms, and can improve image quality while preserving the original phase detail information to a large extent. The denoising performance indicators after filtering are as follows: Figure 5 The right picture and Figure 9 As shown in the right figure, it is further verified that the imaging quality improves with the increase of frame number.

[0126] The present invention obtains multi-frame phase shift images through a multi-frame image acquisition device based on hardware synchronization control, and proposes two multi-frame average phase recovery methods. Figure 5 and Figure 9 As shown in Figure 2, method 2 outperforms method 1 in terms of performance indicators, indicating that its imaging quality is better. In addition, the experiment also compared the computational time of these two multi-frame average phase recovery methods. Figure 6 As shown in , the computation time required for phase recovery increases with the number of frames. In addition, Figure 6 It is also shown that the phase recovery time of method 1 is slightly lower than that of method 2. Therefore, the choice between these two methods should be guided by the application-specific trade-off between processing time and recovery fidelity.

[0127] While the multi-frame averaging shearing speckle interferometry method proposed in this paper can significantly improve imaging quality, it requires acquiring multiple phase-shifted speckle patterns as multi-frame phase shift maps, which is several times more complex than traditional methods. This places stringent demands on system hardware, particularly on camera frame rate and PZT response speed. For real-time or dynamic measurement applications, deploying high-speed cameras and fast-response PZTs is essential.

[0128] The present invention solves the noise problem in shear speckle phase recovery by hardware-assisted multi-frame average phase recovery. By synchronizing the PZT and camera to obtain multi-frame phase shift maps, two new multi-frame average phase recovery methods can be applied. Method 1 calculates the phase difference by directly averaging the multi-frame phase shift maps, while Method 2 initially calculates the phase map of each frame independently and then averages them to calculate the phase difference. Experimental results show that Method 2 is ENL 、 SSI and SMPI It has excellent performance in terms of performance, although the computation time is slightly increased.

[0129] The integration of multi-frame average phase recovery and single-pass 3×3 sine-cosine mean filtering effectively suppresses phase noise while preserving critical phase details. This dual-layer denoising strategy achieves comparable results to traditional multi-pass or complex filtering techniques, with significantly reduced risk of detail loss.

[0130] However, the method's reliance on acquiring multiple, high-fidelity frames introduces hardware limitations, requiring high-speed imaging and high-frequency synchronous drive to accommodate dynamic measurement scenarios. Despite these limitations, the proposed method provides a robust, high-quality imaging solution for shearing speckle interferometry and is equally applicable to other interferometry methods. The proposed method can also be applied to electronic speckle interferometry, laser speckle interferometry, and laser holography.

[0131] In summary, the present invention advances phase recovery in shearing speckle interferometry by developing a multi-frame image acquisition device based on hardware synchronization control and two multi-frame average phase recovery methods, balancing denoising effect, computational efficiency, and phase detail preservation, providing a scalable and universal solution for static and dynamic measurement scenarios.

[0132] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A multi-frame average shearing speckle interferometry method, characterized in that: A multi-frame averaging shearing speckle interferometry system is applied. The system includes an image acquisition device based on hardware synchronization control, a phase recovery device, and a single 3×3 sine-cosine mean filtering device. The method includes: In a multi-frame image acquisition device based on hardware synchronous control, a high-frequency synchronous driver generates a multi-step staircase signal and a high-frequency square wave signal to synchronously control the camera and PZT. The multi-step staircase signal serves as the PZT drive signal to control the PZT displacement, and the square wave signal serves as the camera trigger signal to trigger the camera to capture images through the rising edge. The rising edge or falling edge of each step of the staircase signal is strictly synchronized with the rising edge of the square wave, and multiple camera frame periods correspond to the duration of one step signal. A multi-frame multi-step time phase shift method is used to introduce multiple discrete phase shifts into the PZT. Before and after deformation of the measured object, the camera captures multi-frame phase shift images of the PZT in multiple phase shift intensity modes at the same position, obtaining multi-frame phase shift images in the reference state and multi-frame phase shift images in the deformed state. In the phase recovery device, a multi-frame average pre-phase recovery method or a multi-frame average post-phase recovery method is used to process the multi-frame phase shift image in the reference state and the multi-frame phase shift image in the deformed state to obtain a phase reconstruction image. The multi-frame average pre-phase recovery method calculates the average value of the multi-frame phase shift images in the two states before phase recovery to obtain an average phase shift image, and then calculates an average phase difference image as the phase reconstruction image. The multi-frame average post-phase recovery method averages multiple phase differences in the multi-frame phase shift images in the two states to obtain a final phase difference image as the phase reconstruction image. The single 3×3 sine-cosine mean filtering device is used to perform a single 3×3 sine-cosine mean filtering process on the phase reconstruction image to obtain a filtered image.

2. The method according to claim 1, wherein The multi-frame average pre-phase recovery method specifically includes the following steps: Calculating the average value of multiple frames of phase shift images generated in each phase shift intensity mode under the reference state to obtain multiple average phase shift images under the reference state; Calculating the average value of multiple frames of phase shift images generated in each phase shift intensity mode under the deformation state to obtain multiple average phase shift images under the deformation state; Based on multiple average phase shift images in the reference state and multiple average phase shift images in the deformed state, the average phase difference image caused by the displacement change of the measurement object is calculated.

3. The method according to claim 1, wherein The multi-frame averaging and phase recovery method specifically comprises the following steps: Calculating multiple phase images of multiple frames of phase shift images generated under multiple phase shift intensity modes under a reference state to obtain multiple phase images under a reference state; Calculating multiple phase images of multiple frames of phase shift images generated under multiple phase shift intensity modes under the deformed state to obtain multiple phase images under the deformed state; Calculating multiple phase difference images generated in multiple phase shift intensity modes based on multiple phase images in a reference state and multiple phase images in a deformed state; The multiple phase difference images are averaged to calculate the final phase difference image.

4. The method according to claim 2, wherein When the multi-frame multi-step time phase shift method is specifically a multi-frame four-step phase shift method, the multi-frame average pre-phase recovery method specifically includes the following steps: Calculate the average values of the multi-frame phase shift images generated by the four phase shift intensity modes under the reference state to obtain the four average phase shift images under the reference state. , the calculation formula is: ， in, k Indicates the number of frames, n Indicates the maximum number of frames. Multi-frame phase shift diagrams generated under four phase shift intensity modes when representing the reference state; Calculate the average value of the multi-frame phase shift images generated by the four phase shift intensity modes under the deformation state to obtain four average phase shift images under the deformation state , the calculation formula is: ， in, represents a multi-frame phase shift diagram generated under four phase shift intensity modes in the deformed state; Four average phase shift images based on the reference state , Four average phase shift images under deformation state , calculate the average phase difference image caused by the displacement change of the measurement object , the calculation formula is: ， in, Represents pixel coordinates The average displacement in the vertical direction, Represents pixel coordinates The average displacement in the horizontal direction.

5. The method according to claim 3, wherein When the multi-frame multi-step time phase shift method is specifically a multi-frame four-step phase shift method, the multi-frame averaging and phase recovery method specifically includes: Calculate the multi-frame phase images of the multi-frame phase shift images generated by the four phase shift intensity modes under the reference state to obtain the multi-frame phase images under the reference state , the calculation formula is: ， in, k Indicates the number of frames, n Indicates the maximum number of frames. Multi-frame phase shift diagrams generated under four phase shift intensity modes when representing the reference state; Calculate multiple phase images of the multi-frame phase shift images generated by the four phase shift intensity modes under the deformation state to obtain the multi-frame phase image under the deformation state , the calculation formula is: ， in, Multi-frame phase shift images generated under four phase shift intensity modes when representing the deformation state; Multi-frame phase images based on reference state , multi-frame phase images in deformed state , calculate the multi-frame phase difference images generated by four phase shift intensity modes ; Multi-frame phase difference images Take the average value and calculate the final phase difference image , the calculation formula is: 。 6. The method according to claim 5, wherein The multi-frame phase difference image The first calculation formula is: 。 7. The method according to claim 5, wherein The multi-frame phase difference image The second calculation formula is: ， in, Indicates the average displacement of multiple frames in the vertical direction, Indicates the average displacement of multiple frames in the horizontal direction, The multi-frame phase shift diagram generated in four phase shift intensity modes when representing the reference state, Represents the multi-frame phase shift diagram generated in four phase shift intensity modes in the deformed state, Represents pixel coordinates.

8. The method according to claim 7, wherein When calculating the final phase difference image After that, it also includes: The final phase difference image is used to calculate The formula is simplified, and the calculation formula after simplification is: ， in, Represents multiple frames of simplified phase difference images.

9. The method according to claim 1, wherein After obtaining the phase reconstruction image, the method further includes: Normalized time is used to evaluate the computational efficiency of the multi-frame average pre-phase recovery method and the multi-frame average post-phase recovery method.

10. The method according to claim 1, wherein After obtaining the filtered image, the method further includes: use ENL、SSI 、 SMPI To evaluate the denoising performance of the filtered image.

Images