A core point cloud positioning method based on brightness gradient and related equipment

Abstract

The application discloses a kind of core point cloud positioning methods and related equipment based on brightness gradient, it is related to image processing field, the method includes: based on N frame original image data set is fused to obtain fusion image;Fiber positioning operation is carried out on fusion image, to obtain fiber core coordinate set and fiber core signal intensity set;All fibers are traversed by fiber core coordinate set on fusion image, to obtain the basic region of interest of each fiber;The brightness minimum value of basic region of interest and fiber center point signal intensity are obtained;The brightness gradient value of the rest points except fiber center point in basic region of interest is obtained;Gradient region of interest is determined based on all brightness gradient values;In gradient region of interest, based on fiber center point signal intensity and the brightness minimum value of basic region of interest, determine the brightness threshold value;In gradient region of interest, according to the size relationship of brightness threshold value and the brightness value of each point in region, determine the point cloud of each fiber.

Description

Technical Field

[0001] This specification relates to the field of image processing, and more specifically, this application relates to a fiber core point cloud localization method and related equipment based on brightness gradient. Background Technology

[0002] A microendoscopy is a medical device that, through channels such as gastroscopes and colonoscopes, can be inserted into the human body to obtain local histological images, enabling precise diagnosis of minute lesions, gastrointestinal diseases, and early gastrointestinal cancers. The scanning control module of a microendoscopy has two important components: a resonant mirror and a galvanometer mirror. The resonant mirror's function is to rapidly scan light in the horizontal direction, hence it is also called an X-ray mirror. The galvanometer mirror's function is to scan light in the vertical direction, hence it is also called a Y-ray mirror. Together, they obtain a two-dimensional planar image.

[0003] The resonant mirror and galvanometer mirror contain sophisticated electronic components whose characteristics change with the ambient temperature. During imaging, each image generates a fiber core point cloud based on a predetermined fiber core position. Changes in the mirror characteristics cause variations in the fiber core position during imaging, leading to inaccurate fiber core point cloud localization and consequently, image quality degradation. Therefore, it is necessary to propose a more accurate fiber core point cloud localization method. Summary of the Invention

[0004] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] Firstly, this application proposes a fiber core point cloud localization method based on brightness gradient, the method comprising:

[0006] A fusion operation is performed on an N-frame original image dataset to obtain a fused image;

[0007] Fiber positioning is performed on the above fused image to obtain a fiber core coordinate set and a fiber core signal strength set, wherein the fiber core coordinate set includes the fiber core coordinate value of each fiber core, and the fiber core signal strength set includes the fiber core signal strength value of each fiber core.

[0008] On the fused image, all optical fibers are traversed using the core coordinate set to obtain the basic region of interest for each optical fiber, wherein the region of interest is the pixel value region covering the end face of the optical fiber centered on the core position.

[0009] Obtain the minimum brightness value of the aforementioned basic region of interest and the signal strength at the fiber center point;

[0010] Obtain the brightness gradient values ​​of the remaining points within the aforementioned basic region of interest, excluding the fiber center point;

[0011] The region of interest for the gradient is determined based on all brightness gradient values;

[0012] Within the aforementioned gradient region of interest, a brightness threshold is determined based on the signal strength at the fiber center point and the minimum brightness value of the basic region of interest.

[0013] Within the aforementioned gradient region of interest, the point cloud of each optical fiber is determined based on the brightness threshold and the relationship between the brightness values ​​of each point within the region.

[0014] In one feasible implementation, the fiber positioning operation performed on the fused image to obtain a set of fiber core coordinates and a set of fiber core signal strengths includes:

[0015] The local maximum algorithm is used to perform fiber positioning on the above fused image to obtain the above fiber core coordinate set and the above fiber core signal intensity set.

[0016] In one feasible implementation, obtaining the brightness gradient values ​​of points other than the fiber center point within the basic region of interest includes:

[0017] Based on the above-mentioned basic region of interest, multiple regions of interest are divided with the fiber center point as the reference, wherein each adjacent region of interest is nested with the others.

[0018] Calculate the brightness gradient values ​​for each of the aforementioned regions of interest, excluding the fiber center point.

[0019] In one feasible implementation, the calculation of brightness gradient values ​​for points other than the fiber center point based on each of the aforementioned regions of interest includes:

[0020] Within each of the aforementioned regions of interest, the brightness difference between the current point's own brightness value and the brightness value of its nearest neighbor is used as the brightness gradient of each point, in order to obtain the brightness gradient values ​​of the remaining points other than the fiber center point.

[0021] In one feasible implementation, the above-mentioned determination of the gradient region of interest based on all brightness gradient values ​​includes:

[0022] If the brightness gradient value is greater than 0, the pixel value corresponding to that point is set to zero;

[0023] If the brightness gradient value is less than or equal to 0, the original pixel value is retained;

[0024] The smallest bounding square with pixel values ​​greater than 0 is defined as the region of interest for the gradient described above.

[0025] In one feasible implementation, determining the brightness threshold within the gradient region of interest based on the signal strength at the fiber center point and the minimum brightness value of the basic region of interest includes:

[0026] The brightness threshold Threshold_i is determined according to the following formula:

[0027] Threshold_i=(Value_actual_i-Min_value_i) / M)+Min_value_i

[0028] Where Value_actual_i is the signal strength at the center point of the optical fiber, Min_value_i is the minimum brightness of the basic region of interest, and M is a positive integer.

[0029] In one feasible implementation, determining the point cloud of each optical fiber within the gradient region of interest based on the brightness threshold and the relationship between the brightness values ​​of each point within the region includes:

[0030] Within each of the aforementioned gradient regions of interest, points with brightness values ​​greater than the aforementioned brightness threshold are identified as points in the fiber core cloud of that fiber, thereby determining the point cloud of each fiber.

[0031] Secondly, embodiments of this application propose a fiber core point cloud positioning device based on brightness gradient, comprising:

[0032] The first acquisition unit is used to perform a fusion operation based on the N-frame original image dataset to obtain a fused image;

[0033] The second acquisition unit is used to perform fiber positioning operation on the above-mentioned fused image to obtain a fiber core coordinate set and a fiber core signal strength set, wherein the fiber core coordinate set includes the fiber core coordinate value of each fiber core, and the fiber core signal strength set includes the fiber core signal strength value of each fiber core.

[0034] The third acquisition unit is used to traverse all optical fibers on the above-mentioned fused image through the above-mentioned fiber core coordinate set to obtain the basic region of interest for each optical fiber, wherein the above-mentioned region of interest is a pixel value region covering the end face of the optical fiber centered on the fiber core position.

[0035] The fourth acquisition unit is used to acquire the minimum brightness value of the basic region of interest and the signal strength at the center point of the optical fiber;

[0036] The fifth acquisition unit is used to acquire the brightness gradient values ​​of the remaining points in the basic region of interest, excluding the fiber center point.

[0037] The first determining unit is used to determine the gradient region of interest based on all brightness gradient values;

[0038] The second determining unit is used to determine a brightness threshold in the gradient region of interest based on the signal strength at the center point of the optical fiber and the minimum brightness value of the basic region of interest.

[0039] The third determining unit is used to determine the point cloud of each optical fiber within the gradient region of interest based on the brightness threshold and the relationship between the brightness values ​​of each point in the region.

[0040] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the fiber core point cloud localization method based on brightness gradient as described in any of the first aspects above.

[0041] Fourthly, this application also proposes a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the fiber core point cloud localization method based on brightness gradient of any one of the first aspects.

[0042] In summary, the method proposed in this application significantly enhances signal contrast and detail clarity in images through the fusion of multi-frame image datasets. Advanced image analysis techniques are used to precisely locate each fiber core and measure its signal intensity. This precise location technology ensures imaging consistency and accuracy, especially when the core position may slightly change due to internal temperature variations. By defining a fundamental region of interest (ROI) for each fiber and further analyzing the minimum brightness, signal intensity, and brightness gradient of these regions, it is possible to better understand signal variations within the fiber and to detect and describe subtle tissue structural changes. Based on the gradient ROI and brightness threshold, this method can accurately determine the point cloud of each fiber. These point clouds not only provide a precise two-dimensional image of the fiber but can also be used to generate a three-dimensional representation, providing doctors with a more comprehensive view and more diagnostic information. Accurate image processing and analysis techniques can improve the diagnostic accuracy for early cancers and other gastrointestinal lesions. This is of great significance for improving treatment outcomes, selecting the best treatment methods, and monitoring disease progression. The fiber core end-face positioning method in this application can quickly and accurately locate the fiber core end-face area, ensuring that the fiber core is positioned within the correct region. The fiber core point cloud computing method in this solution can obtain the point cloud region for each fiber core, ensuring that accurate brightness values ​​can be extracted for each fiber core. By providing higher quality images, more precise data analysis, and more advanced technical means, the application effect of microendoscopy in the detection of small lesions and early cancerous changes is significantly improved. This technological advancement provides a more powerful tool for medical diagnosis, making early diagnosis and treatment possible.

[0043] The fiber core point cloud localization method based on brightness gradient proposed in this application, other advantages, objectives and features of this application will be partly apparent from the following description, and partly understood by those skilled in the art through research and practice of this application. Attached Figure Description

[0044] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0045] Figure 1 A flowchart illustrating a fiber core point cloud localization method based on brightness gradient provided in this application embodiment;

[0046] Figure 2 This is a partially enlarged schematic diagram of a fiber core before positioning, provided as an embodiment of this application.

[0047] Figure 3 This is a partially enlarged schematic diagram of a fiber core after positioning, provided as an embodiment of this application.

[0048] Figure 4 A schematic diagram of a basic region of interest provided for an embodiment of this application;

[0049] Figure 5 A schematic diagram of the region of interest provided in an embodiment of this application;

[0050] Figure 6 This is a schematic diagram illustrating the principle of brightness gradient calculation in an embodiment of this application.

[0051] Figure 7 A schematic diagram of a gradient region of interest provided in an embodiment of this application;

[0052] Figure 8 A schematic diagram of an optical fiber point cloud provided in an embodiment of this application;

[0053] Figure 9 This is a magnified view of a point cloud positioning method provided in an embodiment of this application.

[0054] Figure 10 A structural schematic diagram of a fiber core point cloud positioning device based on brightness gradient provided in an embodiment of this application;

[0055] Figure 11 This is a schematic diagram of a fiber core point cloud positioning electronic device provided in an embodiment of this application. Detailed Implementation

[0056] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0057] Please see Figure 1This is a flowchart illustrating a fiber core point cloud localization method based on brightness gradient, provided in an embodiment of this application. Specifically, it may include:

[0058] S110. Perform a fusion operation based on the N frames of original image dataset to obtain a fused image;

[0059] For example, a fusion operation is performed using a dataset of N original images. The aim is to improve image quality and information content by combining multiple frames, thereby better identifying and analyzing optical fibers. Fusion techniques include, but are not limited to, image overlay, multi-frame integration, or high dynamic range (HDR) techniques to enhance the visibility of optical fibers and signal contrast in the image.

[0060] Specifically, under the premise of clear focus and correct configuration parameters, N frames of confocal original image data are acquired, and the original sequence of image data is denoted as S. All images in S are selected for fusion, and the fused image is denoted as Img_fusion. For the fusion image method, this method adopts the mean fusion method, that is, the average value of corresponding pixels in the sequence of images is taken as the pixel value of the corresponding point in the fused image, and then the fused image Img_fusion of the group of images is obtained.

[0061] S120. Perform fiber positioning operation on the above fused image to obtain fiber core coordinate set and fiber core signal strength set, wherein the fiber core coordinate set includes the fiber core coordinate value of each fiber core, and the fiber core signal strength set includes the fiber core signal strength value of each fiber core.

[0062] For example, the fused image is used to locate the optical fiber. The specific location of each fiber core (fiber core coordinate set) and the signal strength value of each fiber core (fiber core signal strength set) are identified. Image analysis techniques, such as edge detection or pattern recognition, are used to accurately locate the center of each fiber core and measure its signal strength.

[0063] S130. Traverse all optical fibers on the above-mentioned fused image using the above-mentioned fiber core coordinate set to obtain the basic region of interest for each optical fiber, wherein the above-mentioned region of interest is a pixel value region covering the end face of the optical fiber centered on the fiber core position.

[0064] For example, after obtaining the location and signal strength of each fiber, the next step is to define the basic region of interest (ROI) for each fiber. These regions are typically fixed-size pixel areas surrounding the fiber core location, used for further analysis and processing.

[0065] S140. Obtain the minimum brightness value of the above-mentioned basic region of interest and the signal strength at the center point of the optical fiber;

[0066] For example, each basic region of interest is analyzed to obtain the minimum brightness within the region and the signal strength at the fiber center point. This is done to determine the brightness threshold and the gradient region of interest.

[0067] Specifically, in the fused image Img_fusion, all fibers are traversed using the fiber core coordinate set Coord. For a single fiber i, an MxN region centered on the fiber coordinates is first extracted, denoted as the region of interest ROI_i. The region of interest ROI_i is as follows: Figure 4 As shown, the minimum brightness value of this area is extracted and denoted as Min_value_i, and the signal strength at the center point of the optical fiber is denoted as Value_actual_i.

[0068] S150. Obtain the brightness gradient values ​​of the remaining points in the above-mentioned basic region of interest, excluding the center point of the optical fiber.

[0069] For example, besides the fiber center point, the brightness gradient values ​​of other points within the basic region of interest are calculated. The brightness gradient reflects the rate of brightness change in the image and is a key indicator for identifying edge and texture information.

[0070] S160. Determine the gradient region of interest based on all brightness gradient values;

[0071] For example, regions of interest are determined based on the calculated brightness gradient values. These regions include the parts where the brightness changes most significantly, corresponding to the edges of the optical fiber or other key features.

[0072] S170. Determine the brightness threshold within the gradient region of interest based on the signal strength at the fiber center point and the minimum brightness value of the basic region of interest.

[0073] For example, within the gradient region of interest, an appropriate brightness threshold is determined based on the signal strength at the fiber's center point and the minimum brightness value of the underlying region of interest. This threshold is used for further analysis and extraction of detailed fiber characteristics.

[0074] S180. Within the region of interest of the gradient mentioned above, determine the point cloud of each optical fiber based on the brightness threshold and the relationship between the brightness values ​​of each point within the region.

[0075] For example, within the gradient region of interest, a point cloud for each optical fiber is determined based on a set brightness threshold and the relationship between the brightness values ​​of each point. The point cloud can be used to form a three-dimensional representation of the optical fiber or for other purposes.

[0076] In summary, the method proposed in this application significantly enhances signal contrast and detail clarity in images through the fusion of multi-frame image datasets. Advanced image analysis techniques are used to precisely locate each fiber core and measure its signal intensity. This precise location technology ensures imaging consistency and accuracy, especially when the core position may slightly change due to internal temperature variations. By defining a fundamental region of interest (ROI) for each fiber and further analyzing the minimum brightness, signal intensity, and brightness gradient of these regions, it is possible to better understand signal variations within the fiber and to detect and describe subtle tissue structural changes. Based on the gradient ROI and brightness threshold, this method can accurately determine the point cloud of each fiber. These point clouds not only provide a precise two-dimensional image of the fiber but can also be used to generate a three-dimensional representation, providing doctors with a more comprehensive view and more diagnostic information. Accurate image processing and analysis techniques can improve the diagnostic accuracy for early cancers and other gastrointestinal lesions. This is of great significance for improving treatment outcomes, selecting the best treatment methods, and monitoring disease progression. The fiber core end-face positioning method in this application can quickly and accurately locate the fiber core end-face area, ensuring that the fiber core is positioned within the correct region. The fiber core point cloud computing method in this solution can obtain the point cloud region for each fiber core, ensuring that accurate brightness values ​​can be extracted for each fiber core. By providing higher quality images, more precise data analysis, and more advanced technical means, the application effect of microendoscopy in the detection of small lesions and early cancerous changes is significantly improved. This technological advancement provides a more powerful tool for medical diagnosis, making early diagnosis and treatment possible.

[0077] In some examples, the fiber positioning operation performed on the fused image to obtain the core coordinate set and the core signal strength set includes:

[0078] The local maximum algorithm is used to perform fiber positioning on the above fused image to obtain the above fiber core coordinate set and the above fiber core signal intensity set.

[0079] For example, local maxima algorithms are used to identify salient feature points in an image. The algorithm identifies points in an image where brightness or color intensity reaches a local maximum; these points often represent the core location of an optical fiber. A local maximum can be a maximum value relative to its surrounding neighborhood.

[0080] Specifically, fiber positioning is performed on the fused image, that is, the local maximum algorithm is used to find the fiber coordinate parameters, and the core coordinate set Coord and the core signal intensity set are denoted as Value_actual.

[0081] The detailed steps for locating optical fibers are as follows: First, a 3x3 anchor frame is preset. The entire image is traversed with a step size of 1. Each time the anchor frame moves, it is checked whether the maximum value within the anchor frame equals the pixel value of the anchor frame's center point. If they are equal, this point is recorded as the center coordinate point of the optical fiber. If not, the movement continues. Finally, the entire image is traversed to locate all optical fibers. A magnified view of the fiber cores before and after positioning on the fused image Img_fusion is shown below. Figure 2 and Figure 3 As shown.

[0082] Local maxima algorithms can accurately identify the location of the fiber core, especially when there is significant contrast between the fiber and the background. This process can be fully automated, reducing human error and subjectivity. Compared to manual marking or more complex image analysis methods, local maxima algorithms are computationally simpler and faster.

[0083] In some examples, obtaining the brightness gradient values ​​of points other than the fiber center point within the aforementioned basic region of interest includes:

[0084] Based on the above-mentioned basic region of interest, multiple regions of interest are divided with the fiber center point as the reference, wherein each adjacent region of interest is nested with the other.

[0085] Calculate the brightness gradient values ​​for each of the aforementioned regions of interest, excluding the fiber center point.

[0086] For example, multiple circular or rectangular regions of interest are divided based on the center point of the optical fiber. Each new region of interest has a larger extent than the previous one, thus forming a nested relationship. This division strategy can be uniformly expanded, i.e., each region is slightly larger than the previous one, or the size of the regions can be gradually increased according to a specific algorithm. Figure 5 As shown, this is a schematic diagram of a scenario where the area of ​​interest is divided. Figures 1 to 5 represent five areas of interest.

[0087] The brightness gradient is a measure of the change in brightness at each point in an image, typically calculated by determining the brightness difference between a point and its neighbors. Within each region of interest, the brightness gradient value is calculated for every point except the center point of the fiber.

[0088] In some examples, the calculation of brightness gradient values ​​for points other than the fiber center point based on each of the aforementioned regions of interest includes:

[0089] Within each of the aforementioned regions of interest, the brightness difference between the current point's own brightness value and the brightness value of its nearest neighbor is used as the brightness gradient of each point, in order to obtain the brightness gradient values ​​of the remaining points other than the fiber center point.

[0090] For example, such as Figure 6The diagram illustrates the principle of brightness gradient calculation. The brightness gradient value is calculated by subtracting the pixel value of the nearest point in L_k-1 from each point in L_k (k>2), thus obtaining the brightness gradient value of each element in L_k. That is, through methods such as... Figure 6 The brightness gradient at a point is the difference between the starting and ending positions of the arrow's direction.

[0091] In some examples, the region of interest for the gradient is determined based on all brightness gradient values, including:

[0092] If the brightness gradient value is greater than 0, the pixel value corresponding to that point is set to zero;

[0093] If the brightness gradient value is less than or equal to 0, the original pixel value is retained;

[0094] The smallest bounding square with pixel values ​​greater than 0 is defined as the region of interest for the gradient described above.

[0095] For example, when the brightness gradient value of a pixel is greater than 0, it indicates that the brightness of that pixel is increasing relative to its neighbors. In this case, to highlight areas where the brightness is decreasing or not changing much, the pixel values ​​of these pixels with increasing brightness are set to zero. This eliminates or ignores the interference of increasing brightness, focusing on areas where the brightness changes little or decreases.

[0096] For points with a brightness gradient value less than or equal to 0, their original pixel values ​​are retained. These points may represent boundaries or feature points in the image, as a decrease or no change in the brightness gradient may indicate important image features. After the above processing, many pixel values ​​in the image will be set to zero, while the remaining non-zero pixels will mark areas that may contain important image information.

[0097] To determine the region of interest, find the smallest bounding square containing all non-zero pixel values. This square contains all points that retain their original pixel values, i.e., those regions with gradient values ​​less than or equal to 0. These regions are considered to be the most critical parts of the image in terms of gradient changes.

[0098] Specifically, such as Figure 7 The diagram shows a gradient region of interest (ROI). Excluding the center point, a brightness gradient value is calculated for each point within the base ROI_i. Based on the calculated brightness gradient value, if the corresponding brightness gradient value for that point in the ROI_i is greater than 0, the pixel value of that point is set to 0, thus obtaining the gradient ROI_i.

[0099] In some examples, the determination of the brightness threshold within the gradient region of interest based on the signal strength at the fiber center point and the minimum brightness value of the basic region of interest includes:

[0100] The brightness threshold Threshold_i is determined according to the following formula:

[0101] Threshold_i=(Value_actual_i-Min_value_i) / M)+Min_value_i

[0102] Where Value_actual_i is the signal strength at the center point of the optical fiber, Min_value_i is the minimum brightness of the basic region of interest, and M is a positive integer.

[0103] For example, the difference between the signal strength at the fiber center point and the minimum brightness of the underlying region of interest (GROUP) is calculated as (Value_actual_i - Min_value_i). This difference represents the range of brightness variation from the darkest to the brightest point. Dividing this difference by M narrows the brightness range, ensuring that the threshold is not too close to the high brightness of the center point, while still sufficiently higher than the darkest brightness, thus ensuring that the threshold effectively separates the fiber center from its surrounding area. M can be set empirically, such as 3, 5, or 8.

[0104] The calculated result is then added to the minimum brightness value, Min_value_i. This is done to ensure that the brightness threshold is at least higher than the brightness of the darkest area, thus more accurately identifying the area around the center point that is slightly brighter than the background.

[0105] The method proposed in this embodiment adaptively determines a suitable brightness threshold based on the signal strength at the fiber center point and the minimum brightness of the underlying region of interest. This ensures that the threshold is neither too high, ignoring important boundary information, nor too low, failing to effectively distinguish the fiber center point from the background.

[0106] In some examples, the point cloud of each fiber is determined within the region of interest of the gradient described above, based on the brightness threshold and the relationship between the brightness values ​​of each point within the region.

[0107] Within each of the aforementioned gradient regions of interest, points with brightness values ​​greater than the aforementioned brightness threshold are identified as points in the fiber core cloud of that fiber, thus determining the point cloud of each fiber.

[0108] For example, the pixel value of each point within the gradient region of interest (Gradient_ROI_i) is compared with its corresponding brightness threshold. Points that are greater than or equal to the threshold are recorded as points in the fiber core cloud. The point cloud obtained for this fiber is as follows: Figure 8 As shown. In addition, to compensate for some optical fibers, the points in the 8-neighborhood of the fiber center point can be set to the points of the fiber core point cloud by default. Figure 9This is a magnified view of the fiber core point cloud localization on the fused image Img_fusion, showing that the localization effect is good.

[0109] Please see Figure 10 The present application provides a structural schematic diagram of a fiber core point cloud positioning device based on brightness gradient, which may include:

[0110] The first acquisition unit 21 is used to perform a fusion operation based on the N-frame original image dataset to obtain a fused image;

[0111] The second acquisition unit 22 is used to perform fiber positioning operation on the above-mentioned fused image to obtain a fiber core coordinate set and a fiber core signal strength set, wherein the fiber core coordinate set includes the fiber core coordinate value of each fiber core, and the fiber core signal strength set includes the fiber core signal strength value of each fiber core.

[0112] The third acquisition unit 23 is used to traverse all optical fibers on the above-mentioned fused image through the above-mentioned fiber core coordinate set to obtain the basic region of interest for each optical fiber, wherein the above-mentioned region of interest is a pixel value region covering the end face of the optical fiber centered on the fiber core position.

[0113] The fourth acquisition unit 24 is used to acquire the minimum brightness value of the basic region of interest and the signal strength at the center point of the optical fiber;

[0114] The fifth acquisition unit 25 is used to acquire the brightness gradient values ​​of the remaining points in the basic region of interest, excluding the center point of the optical fiber.

[0115] The first determining unit 26 is used to determine the gradient interest region based on all brightness gradient values;

[0116] The second determining unit 27 is used to determine a brightness threshold in the gradient region of interest based on the signal strength at the center point of the optical fiber and the minimum brightness value of the basic region of interest.

[0117] The third determining unit 28 is used to determine the point cloud of each optical fiber within the gradient region of interest based on the brightness threshold and the relationship between the brightness values ​​of each point in the region.

[0118] like Figure 11 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-described methods for fiber core point cloud positioning.

[0119] Since the electronic device described in this embodiment is the device used to implement the fiber core point cloud positioning device based on brightness gradient in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application is within the scope of protection of this application.

[0120] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the corresponding implementation methods in the embodiments.

[0121] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0122] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] This application also provides a computer program product, which includes computer software instructions. When the computer software instructions are run on a processing device, the processing device executes the fiber core point cloud positioning process in the corresponding embodiment.

[0127] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0128] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components 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, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0130] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0131] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0132] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0133] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A fiber core point cloud localization method based on brightness gradient, characterized in that, include: A fusion operation is performed on an N-frame original image dataset to obtain a fused image; Fiber positioning is performed on the fused image to obtain a fiber core coordinate set and a fiber core signal strength set, wherein the fiber core coordinate set includes the fiber core coordinate value of each fiber core, and the fiber core signal strength set includes the fiber core signal strength value of each fiber core. The core coordinate set is used to traverse all optical fibers on the fused image to obtain the basic region of interest for each optical fiber, wherein the region of interest is a pixel value region covering the end face of the optical fiber centered on the core position. Obtain the minimum brightness value of the basic region of interest and the signal strength at the fiber center point; Obtain the brightness gradient values ​​of the remaining points within the basic region of interest, excluding the center point of the optical fiber; The region of interest for the gradient is determined based on all brightness gradient values; Within the gradient region of interest, a brightness threshold is determined based on the signal strength at the fiber center point and the minimum brightness value of the basic region of interest. Within the gradient region of interest, the point cloud of each optical fiber is determined based on the brightness threshold and the relationship between the brightness values ​​of each point within the region. The step of determining the gradient region of interest based on all brightness gradient values ​​includes: If the brightness gradient value is greater than 0, the pixel value corresponding to that point is set to zero; If the brightness gradient value is less than or equal to 0, the original pixel value is retained; The smallest bounding square with pixel values ​​greater than 0 is defined as the gradient region of interest.

2. The fiber core point cloud localization method based on brightness gradient according to claim 1, characterized in that, The fiber positioning operation on the fused image to obtain the fiber core coordinate set and the fiber core signal strength set includes: The local maximum algorithm is used to perform fiber positioning on the fused image to obtain the fiber core coordinate set and the fiber core signal strength set.

3. The fiber core point cloud localization method based on brightness gradient according to claim 1, characterized in that, The step of obtaining the brightness gradient values ​​of points other than the fiber center point within the basic region of interest includes: Based on the fundamental region of interest, multiple regions of interest are divided with the fiber center point as the reference, wherein each adjacent region of interest is nested with the others. The brightness gradient value of each point other than the center point of the optical fiber is calculated based on each region of interest.

4. The fiber core point cloud localization method based on brightness gradient according to claim 3, characterized in that, The step of calculating the brightness gradient values ​​of points other than the center point of the optical fiber based on each region of interest includes: Within each region of interest, the brightness difference between the current point's own brightness value and the brightness value of its nearest neighbor is used as the brightness gradient of each point to obtain the brightness gradient values ​​of the remaining points except for the center point of the optical fiber.

5. The fiber core point cloud localization method based on brightness gradient according to claim 1, characterized in that, The process of determining a brightness threshold within the gradient region of interest based on the signal strength at the fiber center point and the minimum brightness value of the basic region of interest includes: The brightness threshold Threshold_i is determined according to the following formula: Threshold_i = (Value_actual_i- Min_value_i) / M) + Min_value_i Where Value_actual_i is the signal strength at the center point of the optical fiber, Min_value_i is the minimum brightness of the basic region of interest, and M is a positive integer.

6. The fiber core point cloud localization method based on brightness gradient according to claim 1, characterized in that, The process of determining the point cloud of each optical fiber within the gradient region of interest, based on the brightness threshold and the relationship between the brightness values ​​of each point within the region, includes: Within each gradient region of interest, points with brightness values ​​greater than the brightness threshold are identified as points in the fiber core cloud of that fiber, thus determining the point cloud of each fiber.

7. A fiber core point cloud positioning device based on brightness gradient, characterized in that, include: The first acquisition unit is used to perform a fusion operation based on the N-frame original image dataset to obtain a fused image; The second acquisition unit is used to perform fiber positioning operation on the fused image to obtain a fiber core coordinate set and a fiber core signal strength set, wherein the fiber core coordinate set includes the fiber core coordinate value of each fiber core, and the fiber core signal strength set includes the fiber core signal strength value of each fiber core. The third acquisition unit is used to traverse all optical fibers on the fused image through the fiber core coordinate set to obtain the basic region of interest for each optical fiber, wherein the region of interest is a pixel value region covering the end face of the optical fiber centered on the fiber core position. The fourth acquisition unit is used to acquire the minimum brightness value of the basic region of interest and the signal strength at the center point of the optical fiber; The fifth acquisition unit is used to acquire the brightness gradient values ​​of the remaining points in the basic region of interest, excluding the center point of the optical fiber; The first determining unit is used to determine the gradient region of interest based on all brightness gradient values; The second determining unit is used to determine a brightness threshold in the gradient region of interest based on the signal strength at the center point of the optical fiber and the minimum brightness value of the basic region of interest. The third determining unit is used to determine the point cloud of each optical fiber within the gradient region of interest based on the brightness threshold and the relationship between the brightness values ​​of each point within the region; The first determining unit is used to determine the gradient region of interest based on all brightness gradient values, specifically for: If the brightness gradient value is greater than 0, the pixel value corresponding to that point is set to zero; If the brightness gradient value is less than or equal to 0, the original pixel value is retained; The smallest bounding square with pixel values ​​greater than 0 is defined as the gradient region of interest.

8. An electronic device, comprising: The memory and processor are characterized in that the processor is used to execute a computer program stored in the memory to implement the steps of the fiber core point cloud localization method based on brightness gradient as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the fiber core point cloud localization method based on brightness gradient as described in any one of claims 1-6.

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