A method, system, medium, and electronic device for processing structured light stripe images.

Abstract

This application provides a method, system, medium, and electronic device for processing structured light stripe images. The method includes: acquiring an original laser stripe image, the original laser stripe image including structured light stripe structural information acquired under different shooting conditions; performing noise reduction filtering on the original laser stripe image to obtain a noise-reduced image; performing image enhancement post-processing on the noise-reduced image to obtain an enhanced response image; and performing automatic thresholding segmentation on the enhanced response image to obtain a post-processed image result. The method of this application can improve the image quality of structured light stripe images.

Description

Technical Field

[0001] This application belongs to the field of image processing technology, and relates to a structured light stripe image processing method, and particularly to a structured light stripe image processing method, system, medium and electronic device. Background Technology

[0002] With the widespread application of structured light 3D measurement technology in industrial inspection, reverse engineering, and cultural relic digitization, laser stripe center extraction is a crucial step determining the accuracy and quality of 3D reconstruction, making the robustness of its algorithm paramount. However, actual measurement environments are often complex and variable. Limited by the inhomogeneity of the surface material of the measured object, its high reflectivity, and interference from ambient lighting, the acquired laser stripe images commonly suffer from excessive noise, low contrast, and severely uneven grayscale distribution along the stripe direction, resulting in degradation. Current traditional methods involve directly extracting the stripe center. However, in images with low contrast and uneven grayscale, this can easily lead to stripe gaps, breaks, and center deviation, affecting subsequent 3D reconstruction. Summary of the Invention

[0003] The purpose of this application is to provide a method, system, medium, and electronic device for processing structured light stripe images to improve the image quality of structured light stripe images.

[0004] In a first aspect, this application provides a structured light stripe image processing method, the structured light stripe image processing method comprising: acquiring an original laser stripe image, the original laser stripe image including structured light stripe structure information acquired under different shooting conditions; performing noise reduction filtering on the original laser stripe image to obtain a noise-reduced image; performing image enhancement post-processing on the noise-reduced image to obtain an enhanced response image; and performing automatic thresholding segmentation on the enhanced response image to obtain a post-processed image result.

[0005] In one implementation of the first aspect, the process of performing image enhancement post-processing on the denoised image to obtain an enhanced response image includes: constructing an image local curvature matrix based on the denoised image; obtaining eigenvalues ​​and eigenvectors of the image local curvature matrix, wherein the eigenvalues ​​include a first eigenvalue and a second eigenvalue, and the first eigenvalue is greater than the second eigenvalue; constructing an image response function based on the first eigenvalue and the second eigenvalue; and performing grayscale mapping on the image response function to obtain the enhanced response image.

[0006] In one implementation of the first aspect, the structured light stripe image processing method further includes: obtaining the intensity of image grayscale change based on the eigenvalues ​​of the local curvature matrix of the image; obtaining the direction of image grayscale change based on the eigenvector direction of the local curvature matrix of the image; and obtaining image structure information based on the intensity of image grayscale change and the direction of image grayscale change.

[0007] In one implementation of the first aspect, the process of obtaining image structure information based on the intensity and direction of the image grayscale change includes: when the first feature value satisfies a high response condition and the second feature value satisfies a low response condition, the current pixel is an image line structure region; when both the first feature value and the second feature value satisfy the high response condition, the current pixel is an image blob region; when both the first feature value and the second feature value satisfy the low response condition, the current pixel is an image flat region.

[0008] In one implementation of the first aspect, the process of performing automatic thresholding on the enhanced response image to obtain a post-processed image result includes: obtaining an initial threshold based on the grayscale value of the enhanced response image; classifying the enhanced response image pixels based on the initial threshold to obtain background pixels and target pixels; updating the threshold of the enhanced response image based on the background pixels and the target pixels to obtain an updated threshold; and determining the tolerance between the initial threshold and the updated threshold to obtain the post-processed image result.

[0009] In one implementation of the first aspect, the process of classifying the enhanced response image pixels based on the initial threshold to obtain background pixels and target pixels includes: when the pixel grayscale value of the current pixel in the enhanced response image is less than the initial threshold, the current pixel is the background pixel; otherwise, the current pixel is the target pixel.

[0010] In one implementation of the first aspect, the process of updating the threshold of the enhanced response image based on the background pixels and the target pixels to obtain an updated threshold includes: processing the background pixels to obtain an average gray value of the background pixels; processing the target pixels to obtain an average gray value of the target pixels; and obtaining the updated threshold based on the average gray value of the background pixels and the average gray value of the target pixels.

[0011] Secondly, this application provides a structured light stripe image processing system, comprising: an image acquisition module for acquiring an original laser stripe image, the original laser stripe image including structured light stripe structure information acquired under different shooting conditions; a noise reduction and filtering module for performing noise reduction and filtering processing on the original laser stripe image to obtain a noise-reduced image; an image enhancement post-processing module for performing image enhancement post-processing on the noise-reduced image to obtain an enhanced response image; and an image result acquisition module for performing automatic threshold segmentation on the enhanced response image to obtain a post-processed image result.

[0012] Thirdly, this application provides an electronic device, the electronic device comprising: a memory storing a computer program thereon; and a processor communicatively connected to the memory for executing the computer program to implement the above-described structured light stripe image processing method.

[0013] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the above-described structured light stripe image processing method.

[0014] As described above, the structured light stripe image processing method, system, medium, and electronic device described in this application have the following beneficial effects:

[0015] In summary, the structured light stripe image processing method of this application enhances the original laser stripe image and constructs an image response function to achieve a large response value when laser stripe structures are present in the image, while suppressing corner and flat regions. Furthermore, automatic thresholding segmentation of the enhanced response image removes noise interference still present in the enhanced response function. By preprocessing the original laser stripe image, the line structure in the laser stripe image is effectively enhanced, more effective information of the laser stripe features is preserved, image contrast and quality are improved, and noise is reduced. Attached Figure Description

[0016] Figure 1 The diagram shows a process schematic of the structured light stripe image processing method described in the embodiments of this application.

[0017] Figure 2 The diagram shown is a schematic representation of the process of acquiring an enhanced response image as described in an embodiment of this application.

[0018] Figure 3 This is a schematic diagram illustrating the process of obtaining post-processed image results as described in an embodiment of this application.

[0019] Figure 4 and Figure 5The diagram shown is a verification result of the structured light stripe image processing method described in the embodiments of this application.

[0020] Figure 6 The diagram shown is a structural schematic of the structured light stripe image processing system described in an embodiment of this application.

[0021] Figure 7 The diagram shown is a structural schematic of the electronic device described in an embodiment of this application. Detailed Implementation

[0022] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0023] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0024] With the widespread application of structured light 3D measurement technology in industrial inspection, reverse engineering, and cultural relic digitization, laser stripe center extraction is a crucial step determining the accuracy and quality of 3D reconstruction, making the robustness of its algorithm paramount. However, actual measurement environments are often complex and variable. Limited by the inhomogeneity of the surface material of the measured object, its high reflectivity, and interference from ambient lighting, the acquired laser stripe images generally suffer from excessive noise, low contrast, and severely uneven grayscale distribution along the stripe direction, resulting in degradation. To address these issues, post-processing techniques for structured light stripe images primarily focus on accurately separating the laser center, representing depth information, from the background. Currently, the mainstream algorithms include center stripe extraction and centroid stripe extraction based on grayscale weighting. While these two methods can achieve high sub-pixel accuracy under ideal conditions, they are essentially strategies that directly extract the stripe center, heavily relying on the extreme grayscale features of the image or accurate threshold segmentation. This leads to significant limitations in existing technologies when processing low-quality images: First, they lack noise resistance; excessive noise can interfere with extreme point calculations or centroid shifts, causing the extracted center coordinates to deviate from their true position. Second, they are extremely sensitive to uneven grayscale and low-contrast regions; when stripe intensity weakens or is interfered with by background light, the algorithm is prone to misjudgment, resulting in severe stripe loss and line breaks in the extraction results. These breaks not only disrupt the continuity of the stripes but also directly affect the subsequent stitching and reconstruction of 3D point clouds, causing holes or geometric distortions in the model. Therefore, traditional direct extraction methods often struggle to balance accuracy and integrity when dealing with low-quality laser stripe images in complex scenes, becoming a technical bottleneck restricting the further promotion and application of structured light measurement systems in industrial settings.

[0025] At least in response to the above-mentioned problems, the following embodiments of this application provide a structured light stripe image processing method, system, medium, and electronic device.

[0026] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0027] Figure 1 The diagram shows a process schematic of a structured light stripe image processing method according to an embodiment of this application. Figure 1 As shown, the structured light stripe image processing method includes the following steps S11 to S14.

[0028] Step S11: Obtain the original laser stripe image. The original laser stripe image includes structured light stripe structure information acquired under different shooting conditions.

[0029] Step S12: Perform noise reduction filtering on the original laser stripe image to obtain a noise-reduced image. The noise reduction filtering method can be, for example, Gaussian filtering or bilateral filtering.

[0030] Step S13: Perform image enhancement post-processing on the denoised image to obtain an enhanced response image.

[0031] Figure 2 This is a schematic diagram illustrating the process of acquiring an enhanced response image in one embodiment of this application. For example... Figure 2 As shown, the process of performing image enhancement post-processing on the denoised image to obtain an enhanced response image includes the following steps S21 to S24.

[0032] Step S21: Construct a local curvature matrix of the image based on the denoised image.

[0033] For example, the local curvature matrix of a point (x, y) in the denoised image is represented as:

[0034] .

[0035] in, This is represented as the image after noise reduction. Represented as the local curvature matrix of the image, it represents the second derivative of the image at each location, i.e., the information of the local curvature of the image.

[0036] Step S22: Obtain the eigenvalues ​​and eigenvectors of the local curvature matrix of the image. The eigenvalues ​​include a first eigenvalue and a second eigenvalue, wherein the first eigenvalue is greater than the second eigenvalue.

[0037] For example, the feature values ​​are obtained from the local curvature matrix of the image, and are represented as follows:

[0038] .

[0039] in, , The first eigenvalue, This is the second eigenvalue.

[0040] Step S23: Construct an image response function based on the first feature value and the second feature value.

[0041] For example, in order to enhance the line structure information of the image and suppress corner areas and flat areas, based on the first feature value Second eigenvalue The image response function is constructed as follows:

[0042] .

[0043] in, This represents the response value of the image after noise reduction. Based on the image response function... This can guarantee that When the value is large, a larger response value is obtained, while simultaneously , When the value is large, a smaller response value is obtained, thus obtaining enhanced line structure information.

[0044] Step S24: Perform grayscale mapping on the image response function to obtain the enhanced response image. Normalize the response value of the denoised image to obtain a normalized result; perform grayscale mapping on the normalized result to obtain the enhanced response image.

[0045] For example, the response values ​​of the denoised image are normalized to [0,1], and then mapped to different grayscale ranges ([0,255] or [0,65535]) according to actual needs to obtain an enhanced response image. This enhanced response image reduces the inhomogeneity of the original laser stripe image and enhances the line structure in the image.

[0046] In one embodiment of this application, the structured light stripe image processing method further includes the following steps S31 to S33.

[0047] Step S31: Obtain the intensity of image grayscale change based on the eigenvalues ​​of the local curvature matrix of the image.

[0048] Step S32: Obtain the direction of image grayscale change based on the eigenvector direction of the local curvature matrix of the image.

[0049] Step S33: Obtain image structure information based on the intensity of the image grayscale change and the direction of the image grayscale change.

[0050] For example, when the first feature value When the first eigenvalue is the largest eigenvalue, the eigenvector corresponding to the first eigenvalue is... The direction representing the maximum local curvature of an image is the direction of the most drastic change in image grayscale (normal direction). For example, at an edge, it is the direction perpendicular to the edge; in a line structure, it is the direction perpendicular to the line structure; and at a speckle, it is any direction.

[0051] When the second eigenvalue When the minimum eigenvalue is reached, the eigenvector corresponding to the second eigenvalue is... This represents the direction of minimum local curvature in an image, and is the direction where gray-level changes are most gradual (tangent direction). For example, at an edge, it is the direction parallel to the edge; in a line structure, it is the direction along the line structure; at a speckle, it is the direction parallel to the edge. Any vertical direction.

[0052] In one embodiment of this application, the process of obtaining image structure information based on the intensity of the image grayscale change and the direction of the image grayscale change includes: when the first feature value satisfies a high response condition and the second feature value satisfies a low response condition, the current pixel is an image line structure region; when both the first feature value and the second feature value satisfy the high response condition, the current pixel is an image blob region; when both the first feature value and the second feature value satisfy the low response condition, the current pixel is an image flat region.

[0053] For example, a high response condition is when the eigenvalue is a large value, and a low response condition is when the eigenvalue is close to 0. When the first eigenvalue... For a larger value and the second eigenvalue When the value is close to 0, it indicates that the enhanced response image is only in one direction. The changes are drastic, corresponding to the position of the line structure in the image. When the first feature value Second eigenvalue When the first feature value is large, it indicates that the enhanced response image shows strong changes at that point along two mutually perpendicular directions, corresponding to the corner or spot location of the image. Second eigenvalue When both are close to 0, it indicates that the image changes gently around that point, corresponding to a flat region of the image.

[0054] Step S14: Perform automatic thresholding on the enhanced response image to obtain the post-processed image result.

[0055] Figure 3 This is a schematic diagram illustrating the process of obtaining post-processed image results in one embodiment of this application. For example... Figure 3 As shown, the process of performing automatic thresholding on the enhanced response image to obtain the post-processed image result includes the following steps S41 to S44.

[0056] Step S41: Obtain an initial threshold based on the grayscale value of the enhanced response image.

[0057] Step S42: Based on the initial threshold, perform image pixel classification on the enhanced response image to obtain background pixels and target pixels.

[0058] In one embodiment of this application, the process of classifying the enhanced response image into background pixels and target pixels based on the initial threshold includes: when the pixel grayscale value of the current pixel in the enhanced response image is less than the initial threshold, the current pixel is the background pixel; otherwise, the current pixel is the target pixel.

[0059] Step S43: Update the threshold of the enhanced response image based on the background pixels and the target pixels to obtain an updated threshold.

[0060] The process of updating the threshold of the enhanced response image based on the background pixels and the target pixels to obtain an updated threshold includes: processing the background pixels to obtain the average gray value of the background pixels; processing the target pixels to obtain the average gray value of the target pixels; and obtaining the updated threshold based on the average gray value of the background pixels and the average gray value of the target pixels.

[0061] Step S44: Determine the tolerance of the difference between the initial threshold and the updated threshold to obtain the post-processed image result.

[0062] For example, the process of automatically thresholding an enhanced response image can be represented as:

[0063] (1) Select the average gray value of the enhanced response image or the median value of the gray histogram as the initial threshold T.

[0064] (2) Classify the enhanced response image pixels according to the initial threshold. When the pixel gray value is less than the initial threshold, it is classified as a background pixel; otherwise, it is classified as a target pixel. Specifically, it is expressed as follows:

[0065] C1 = {pixels | pixel grayscale value < T} (usually considered as background)

[0066] C2 = {pixels | pixel grayscale value >= T} (usually considered the target)

[0067] (3) Calculate the average gray value of the background pixels and the target pixels to obtain the average gray value of the background pixels. and the average gray value of the target pixel The average gray value of the background pixels and the average gray value of the target pixels are represented by the center of two class centers.

[0068] (4) Obtain the update threshold based on the average gray value of the background pixels and the average gray value of the target pixels. Specifically, it is expressed as:

[0069] .

[0070] (5) Tolerance determination is performed on the threshold difference between the initial threshold and the updated threshold. When the threshold difference... If the value is less than the preset tolerance value, then convergence is considered achieved. If the threshold is the optimal threshold, then let T = T_new and return to (2) to continue iterative optimization.

[0071] By performing automatic thresholding on the enhanced response image, the part with gray level above the threshold is retained and the part with gray level below the threshold is removed, thereby eliminating the noise effect in the enhanced response image.

[0072] To demonstrate the effectiveness of the structured light stripe image processing method of this application for different original laser stripe images, a detailed description will be provided below with reference to two embodiments and their corresponding effect diagrams.

[0073] Example 1: Figure 4 This demonstrates the visual enhancement effect of the structured light stripe image processing method proposed in this application on low-quality original images. For example... Figure 4 As shown on the left, the original laser stripe image was acquired in a complex, high-noise environment. It is evident that there is significant random noise interference in the background, and the laser stripe structure within the target area is unclear, with blurred edges and a low signal-to-noise ratio, directly impacting the accuracy of subsequent feature point recognition. The method described in this application processes this original laser stripe image. First, noise reduction filtering is applied to effectively remove high-frequency noise interference from the background. Then, image enhancement post-processing is used to specifically enhance the contrast between the laser stripes and the background, highlighting the linear structural features of the stripes. (Comparison) Figure 4 As can be seen from the processed image on the right, the noise in the original image has been significantly removed, the edges of the laser stripes have become sharper and more continuous, the line structure has been effectively enhanced, and the overall image quality has been significantly improved, laying a good data foundation for subsequent high-precision feature extraction.

[0074] Example 2: Figure 5 The image shows the application effect of the method in this application in the task of laser stripe center extraction, especially its ability to solve the problem of stripe breakage. For example... Figure 5 As shown on the left, traditional center extraction directly from the original laser stripe image suffers from limitations such as uneven grayscale, low contrast, and local noise interference in the original image. This results in severe stripe interruptions, discontinuous lines, and some center points deviating from their true positions. These missing geometric features lead to holes or distortions in the 3D reconstruction model. By employing the structured light stripe image processing method of this application, the original image is first processed through noise reduction, enhancement, and automatic thresholding to obtain a post-processed image; then, the laser stripe center is extracted from the post-processed image. (Comparison) Figure 5 As can be seen from the extraction results on the right, the method of this application makes up for the grayscale defects of the original image, repairs the stripe interruption phenomenon in the left image, and the extracted laser stripe center lines are complete, smooth and clear, which not only preserves the detailed features, but also greatly improves the connectivity and accuracy of center extraction.

[0075] In summary, the structured light fringe image processing method of this application enhances the original laser fringe image and constructs an image response function to achieve a large response value when laser fringe structures are present in the image, while suppressing corner and flat areas. Automatic thresholding segmentation of the enhanced response image removes noise interference still present in the enhanced response function. By preprocessing the original laser fringe image, the line structure in the laser fringe image is effectively enhanced, more effective information of the laser fringe features is preserved, image contrast and quality are improved, and noise is reduced. No hardware design modifications are required, reducing product costs and improving the accuracy of 3D positioning.

[0076] The protection scope of the structured light stripe image processing method described in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the protection scope of this application.

[0077] This application also provides a structured light stripe image processing system, which can implement the structured light stripe image processing method described in this application. However, the implementation device of the structured light stripe image processing method described in this application includes, but is not limited to, the structure of the structured light stripe image processing system listed in this embodiment. All structural modifications and substitutions of the prior art made in accordance with the principles of this application are included within the protection scope of this application.

[0078] Figure 6 The diagram shown is a structural schematic of a structured light stripe image processing system according to an embodiment of this application. Figure 6 As shown, the structured light stripe image processing system 1 includes an image acquisition module 11, a noise reduction and filtering module 12, an image enhancement and post-processing module 13, and an image result acquisition module 14. The image acquisition module 11 acquires the original laser stripe image, which includes structured light stripe information acquired under different shooting conditions. The noise reduction and filtering module 12 performs noise reduction and filtering on the original laser stripe image to obtain a noise-reduced image. The image enhancement and post-processing module 13 performs image enhancement post-processing on the noise-reduced image to obtain an enhanced response image. The image result acquisition module 14 performs automatic thresholding segmentation on the enhanced response image to obtain the post-processed image result.

[0079] It should be noted that, Figure 6 The modules in the structured light stripe image processing system 1 shown are... Figure 1 The steps in the structured light stripe image processing method correspond one-to-one, and will not be elaborated here.

[0080] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0081] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0082] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0083] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the structured light stripe image processing method provided in this application. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0084] This application embodiment may also provide an electronic device. Figure 7 The diagram shown is a structural schematic of an electronic device 200 according to an embodiment of this application. Figure 7 As shown, in this embodiment, the electronic device 200 includes a memory 201 and a processor 202.

[0085] The memory 201 is used to store computer programs. In some possible implementations, the memory 201 may include various media capable of storing program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disk.

[0086] In this embodiment, memory 201 may include a computer system readable medium in the form of volatile memory, such as RAM and / or cache memory. Electronic device 200 may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 201 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0087] The processor 202 is connected to the memory 201 and is used to execute the computer program stored in the memory 201 so that the electronic device 200 performs the structured light stripe image processing method.

[0088] For example, processor 202 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. In other embodiments, processor 202 may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0089] In some implementations, the electronic device 200 provided in this application embodiment may further include a display 203. The display 203 is communicatively connected to the memory 201 and the processor 202, and is used to display the relevant graphical user interface (GUI) of the structured light stripe image processing method.

[0090] In this embodiment, the display 203 may include a display screen (display panel). In some implementations, the display panel may be configured using a liquid crystal display (LCD), an organic light-emitting diode (OLED), or other similar forms. Furthermore, the display 203 may also be a touch panel (touchscreen, touch screen), which may include a display screen and a touch-sensitive surface. When the touch-sensitive surface detects a touch operation on or near it, it transmits the information to the processor 202 to determine the type of touch event. Subsequently, the processor 202 provides corresponding visual output on the display device based on the type of touch event.

[0091] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0092] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A structured light fringe pattern processing method, characterized in that, The structured light fringe image processing method comprises: An original laser fringe image is acquired, and the original laser fringe image comprises structured light fringe structure information collected under different shooting conditions; Noise reduction filtering processing is performed on the original laser fringe image to obtain a noise-reduced image; Image enhancement post-processing is performed on the noise-reduced image to obtain an enhanced response image; Automatic threshold segmentation is performed on the enhanced response image to obtain a post-processed image result.

2. The structured light stripe image processing method of claim 1, wherein, The process of performing image enhancement post-processing on the noise-reduced image to obtain an enhanced response image comprises: An image local curvature matrix is constructed based on the noise-reduced image; Eigenvalues and eigenvectors of the image local curvature matrix are acquired, and the eigenvalues comprise a first eigenvalue and a second eigenvalue, the first eigenvalue being greater than the second eigenvalue; An image response function is constructed based on the first eigenvalue and the second eigenvalue; Gray mapping is performed on the image response function to obtain the enhanced response image.

3. The structured light stripe image processing method of claim 2, wherein, The structured light fringe image processing method further comprises: An image gray variation intensity is acquired according to the eigenvalue size of the image local curvature matrix; An image gray variation direction is acquired according to the eigenvector direction of the image local curvature matrix; Image structure information is acquired based on the image gray variation intensity and the image gray variation direction.

4. The structured light stripe image processing method of claim 3, wherein, The process of acquiring image structure information based on the image gray variation intensity and the image gray variation direction comprises: When the first eigenvalue satisfies a high response condition and the second eigenvalue satisfies a low response condition, a current pixel point is an image line structure region; When the first eigenvalue and the second eigenvalue both satisfy a high response condition, the current pixel point is an image spot region; When the first eigenvalue and the second eigenvalue both satisfy a low response condition, the current pixel point is an image flat region.

5. The structured light stripe image processing method of claim 1, wherein, The process of performing automatic threshold segmentation on the enhanced response image to obtain a post-processed image result comprises: An initial threshold is acquired according to the gray value of the enhanced response image; Image pixel classification is performed on the enhanced response image based on the initial threshold to obtain background pixels and target pixels; An update threshold is acquired by updating the threshold of the enhanced response image based on the background pixels and the target pixels; A difference between the initial threshold and the update threshold is determined to obtain the post-processed image result.

6. The structured light stripe image processing method of claim 5, wherein, The process of performing image pixel classification on the enhanced response image based on the initial threshold to obtain background pixels and target pixels comprises: When the pixel gray value of a current pixel in the enhanced response image is less than the initial threshold, the current pixel is the background pixel, otherwise, the current pixel is the target pixel.

7. The structured light stripe image processing method of claim 5, wherein, The process of acquiring an update threshold by updating the threshold of the enhanced response image based on the background pixels and the target pixels comprises: The background pixels are processed to obtain a background pixel average gray value; The target pixels are processed to obtain a target pixel average gray value; The update threshold is acquired based on the background pixel average gray value and the target pixel average gray value.

8. A structured light stripe image processing system, characterized by, The structured light fringe image processing system comprises: The image acquisition module is used to acquire the original laser stripe image, which includes structured light stripe structure information acquired under different shooting conditions; The noise reduction and filtering module is used to perform noise reduction and filtering on the original laser stripe image to obtain a noise-reduced image. An image enhancement post-processing module is used to perform image enhancement post-processing on the denoised image to obtain an enhanced response image; The image result acquisition module is used to perform automatic thresholding segmentation on the enhanced response image to obtain post-processed image results.

9. An electronic device, comprising: The electronic device includes: A memory on which computer programs are stored; A processor, communicatively connected to the memory, is used to execute the computer program to implement the structured light stripe image processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When executed by an electronic device, the program implements the structured light stripe image processing method as described in any one of claims 1 to 7.

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