Multi-core optical fiber alignment method and device based on fiber core edge

Abstract

The invention relates to the technical field of multi-core optical fiber automatic alignment, in particular to a multi-core optical fiber alignment method and device based on a fiber core edge, which mainly focuses on a fiber core edge image on an image instead of overall brightness by acquiring the fiber core edge image and performing an edge cross-correlation algorithm through the fiber core edge image. Therefore, even if the welding end face of the multi-core optical fiber has scratches, stains or other damages, the optical fiber can be accurately aligned, and the influence of factors such as damage to the end face of the optical fiber and uneven brightness can be effectively eliminated; on the other hand, the edge cross-correlation algorithm can effectively eliminate common correlation false peaks in a conventional cross-correlation algorithm and reduce misjudgment conditions, and the edge cross-correlation algorithm significantly reduces the occurrence of false peaks and improves the accuracy of optical fiber alignment by extracting the edge features of the image; by accurately aligning the multi-core optical fibers, perfect contact between the end faces of the optical fibers can be ensured, so that loss in signal transmission is reduced, and the service life of an optical fiber system is prolonged.

Description

Technical Field

[0001] The invention relates to the technical field of multi-core optical fiber automatic alignment, and in particular to a multi-core optical fiber alignment method and device based on fiber core edges. Background Art

[0002] Due to its unique advantages, multi-core optical fiber has been widely used in many high-tech fields such as optical communication, fiber sensing, fiber laser and high-precision measurement. In this context, it is particularly important to achieve high-quality splicing of multi-core optical fibers, which is directly related to the actual performance and reliability of these applications. Unlike single-mode optical fibers, multi-core optical fibers have an internal structure that contains multiple cores. Multiple cores are arranged in a single cladding according to the principle of geometric symmetry, aiming to minimize mutual interference between cores. Each core in a multi-core optical fiber is an independent optical transmission channel, which also makes the end face of the multi-core optical fiber have a more complex structure. In order to ensure that each channel can effectively transmit signals, all cores must be accurately aligned during splicing. Any slight deviation will cause the optical signal to leak or be lost between the cores, causing crosstalk or signal attenuation, affecting the quality and efficiency of data transmission.

[0003] The multi-core optical fiber splicing methods mainly include active alignment methods and passive alignment methods. The active alignment method achieves precise alignment by using a light source and an optical power meter, but the equipment is complex and costly, and is suitable for laboratories and specific indoor environments. The passive alignment method utilizes image processing technology, including side image and end face image alignment. The side image alignment requires obtaining images at different angles when the fusion splicer rotates, which has high requirements for real-time control accuracy and the images are complex and difficult to process. The end face image alignment has the characteristics of wide applicability and high precision, and is suitable for the alignment of multi-core optical fibers.

[0004] At present, the mainstream algorithms for processing end-face images have small differences between correlation peaks when calculating correlation. When the processing accuracy is insufficient or affected by other factors (such as brightness, cladding damage), the alignment angle obtained may not be the optimal solution, thus affecting the splicing effect of multi-core optical fibers.

[0005] In view of this, overcoming the defects of the prior art is an urgent problem to be solved in the field of this technology. Summary of the invention

[0006] The technical problem to be solved by the present invention is how to improve the connection accuracy of multi-core optical fibers.

[0007] The present invention adopts the following technical solution: In a first aspect, a multi-core optical fiber alignment method based on a core edge is provided, comprising: Acquire a first end face image of the first multi-core optical fiber and a second end face image of the second multi-core optical fiber; filtering the first end surface image to obtain a first filtered image, and filtering the second end surface image to obtain a second filtered image; Acquire a first fiber core edge image on the first filtered image, and acquire a second fiber core edge image on the second filtered image; Rotate the first fiber core edge image angle by angle, and perform cross-correlation calculation on the rotated first fiber core edge image and the second fiber core edge image respectively, until the best alignment angle corresponding to the maximum value of the cross-correlation coefficient is obtained; The first multi-core optical fiber is rotated to the optimal alignment angle to align the first multi-core optical fiber with the second multi-core optical fiber.

[0008] Preferably, the acquiring of a first end face image of the first multi-core optical fiber and a second end face image of the second multi-core optical fiber comprises: Stripping the coating layers of the fusion ends of the first multi-core optical fiber and the second multi-core optical fiber, and neatly cutting the fusion end faces of the first multi-core optical fiber and the second multi-core optical fiber, respectively placing the first multi-core optical fiber and the second multi-core optical fiber into the clamps at both ends of the fusion splicer and fixing them; Adjusting the positions of the fusion-joined end faces of the first multi-core optical fiber and the second multi-core optical fiber, and obtaining a first intermediate image of the first multi-core optical fiber and a second intermediate image of the second multi-core optical fiber through end face imaging of a fusion-joining machine; Performing grayscale conversion on the first intermediate image to obtain a first grayscale image, and performing grayscale conversion on the second intermediate image to obtain a second grayscale image; The first grayscale image and the second grayscale image are adjusted to the same size to obtain the first end surface image and the second end surface image.

[0009] Preferably, adjusting the first grayscale image and the second grayscale image to the same size to obtain the first end surface image and the second end surface image comprises: Finding a circular edge of the first grayscale image, and obtaining a first center coordinate and a first optical fiber pixel size of the first multi-core optical fiber; Finding the circular edge of the second grayscale image, and obtaining the second center coordinates and the second optical fiber pixel size of the second multi-core optical fiber; A first square image with the first center coordinate at the center of the first end face image is cut out according to the first center coordinate and the first optical fiber pixel size; A second square image with the second center coordinate at the center of the second end face image is cut out according to the second center coordinate and the second optical fiber pixel size; The sizes of the first square image and the second square image are adjusted to be consistent, so as to obtain the first end surface image and the second end surface image.

[0010] Preferably, the acquiring a first fiber core edge image on the first filtered image comprises: Obtaining the gradient magnitude and gradient direction of each pixel in the first filtered image; Compare the gradient amplitude of each pixel with that of its adjacent pixels in the gradient direction. If the gradient amplitude of the current pixel is less than the gradient amplitude of any adjacent pixel, set the gradient amplitude of the current pixel to 0; if the gradient amplitude of the current pixel is greater than or equal to the gradient amplitudes of all adjacent pixels, retain the gradient amplitude corresponding to the current pixel. Preset a high threshold and a low threshold, determine the pixel points with a gradient amplitude greater than the high threshold as strong edge pixels, determine the pixel points with a gradient amplitude less than the low threshold as non-edge pixels, and set the gradient amplitude to 0; For a pixel point whose gradient amplitude is between the high threshold and the low threshold, if the pixel point is connected to the strong edge pixel point, the pixel point is determined as a strong edge pixel point; All the strong edge pixel points are connected together in sequence to obtain the first fiber core edge image.

[0011] Preferably, the calculation formula of the mutual correlation coefficient is:

[0012] Where R is the mutual correlation coefficient, and are respectively the image matrices of the first core edge image and the image matrix of the second core edge image The number of rows and columns, yes The average value of yes The x-axis is the horizontal axis and the y-axis is the vertical axis.

[0013] Preferably, the filtering process of the first end face image and the second end face image is the same, and filtering the first end face image to obtain the first filtered image includes: Performing logarithmic transformation and Fourier transformation on the first end surface image to obtain a second intermediate image; filtering the second intermediate image by a filter, and performing an inverse Fourier transform on the filtered second intermediate image to obtain a third intermediate image; Perform an exponential transformation on the third intermediate image to obtain the first filtered image.

[0014] Preferably, the expression of the filter is:

[0015] Where n is the order of the filter, is the cutoff frequency of the filter, is the high frequency gain, is the low frequency gain, is the square root function, i and j are the filter coefficients.

[0016] Preferably, the inverse Fourier transform formula is:

[0017] The exponential transformation formula is:

[0018] Where F is the Fourier transform, is the second intermediate image after filtering, is the third intermediate image, is an exponential function.

[0019] In a second aspect, a multi-core optical fiber alignment device based on a core edge is provided, the multi-core optical fiber alignment device based on a core edge comprises: a processor and a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the multi-core optical fiber alignment method based on core edge described in the first aspect.

[0020] In a third aspect, a non-volatile computer storage medium is provided, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are executed by one or more processors to complete the multi-core optical fiber alignment method based on the core edge described in the first aspect.

[0021] In a fourth aspect, a chip is provided, comprising: a processor and an interface, for calling and running a computer program stored in the memory from a memory, and executing the multi-core optical fiber alignment method based on the core edge as described in the first aspect.

[0022] In a fifth aspect, a computer program product comprising instructions is provided, which, when executed on a computer or a processor, enables the computer or the processor to execute the multi-core optical fiber alignment method based on the core edge as described in the first to fourth aspects and any one of the aspects.

[0023] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains a multi-core optical fiber end face image, then processes the end face image to obtain a core edge image, and performs an edge cross-correlation algorithm on the core edge image, focusing mainly on the core edge image on the image rather than the overall brightness. Therefore, even if there are scratches, stains or other damages on the fusion end face of the multi-core optical fiber, the optical fiber can be accurately aligned, and the influence of factors such as optical fiber end face damage and uneven brightness can be effectively eliminated; on the other hand, the edge cross-correlation algorithm can effectively eliminate the common correlation pseudo-peaks in conventional cross-correlation algorithms and reduce misjudgment. The edge cross-correlation algorithm significantly reduces the occurrence of pseudo-peaks by extracting edge features of the image, thereby improving the accuracy of optical fiber alignment; by accurately aligning the multi-core optical fiber, perfect contact between the optical fiber end faces can be ensured, thereby reducing the loss in signal transmission and extending the service life of the optical fiber system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 It is a schematic flow chart of a multi-core optical fiber alignment method based on fiber core edges provided by an embodiment of the present invention; Figure 2 is a schematic diagram of a process for obtaining an end face image provided by an embodiment of the present invention; Figure 3 is a schematic diagram of an end face image provided by an embodiment of the present invention; Figure 4 is a more specific flow chart of obtaining an end face image provided by an embodiment of the present invention; Figure 5 is a schematic diagram of extracting the edge of the cladding provided by an embodiment of the present invention; Figure 6 is a schematic diagram of a cropped square image provided by an embodiment of the present invention; Figure 7 is a schematic diagram of a filtered image provided by an embodiment of the present invention; Figure 8 is a schematic diagram of a fiber core edge image provided by an embodiment of the present invention; Fig. 9 is a schematic diagram of the relationship between a rotation angle and a mutual correlation coefficient provided by an embodiment of the present invention; Fig.10 It is a schematic diagram of the fusion splicing of a seven-core optical fiber provided by an embodiment of the present invention; Fig.11It is a schematic diagram of fusion splicing of a four-core optical fiber provided by an embodiment of the present invention; Fig.12 It is a structural schematic diagram of a multi-core optical fiber alignment device based on fiber core edges provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] Unless the context requires otherwise, throughout the specification and claims, the term "including" is to be interpreted as open inclusion, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples" and the like are intended to indicate that specific features, structures, materials or characteristics associated with the embodiment or example are included in at least one embodiment or example of the present disclosure. The schematic representation of the above terms does not necessarily refer to the same embodiment or example. In addition, the specific features, structures, materials or characteristics may be included in any one or more embodiments or examples in any appropriate manner, that is, although they may be carried in the embodiments or examples of the above terms due to reasons such as the order and position of appearance, it is not limited to that they can be carried in combination by one embodiment or example.

[0028] In the description of the present invention, the terms "first" and "second" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise specified, the meaning of "multiple" is two or more. In addition, for example, the same type of nouns may be described as two independent individuals by adding "A" and "B" at the end. In this case, the corresponding features defined as "A" and "B" are only used to distinguish the same type of individuals for description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features.

[0029] When describing some embodiments, the expressions "coupling", "coupling" and "connection" and their derivatives may be used. For example, when describing some embodiments, the term "connection" may be used to indicate that two or more components are in direct physical or electrical contact with each other. For another example, when describing some embodiments, the term "coupling" may be used to indicate that two or more components are in direct physical or electrical contact. However, the terms "connection" or "coupling" may also refer to two or more components that are not in direct contact with each other, but still cooperate or interact with each other, such as "optical path coupling", "wireless connection", etc. The embodiments disclosed herein are not necessarily limited to the contents of the present invention.

[0030] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0031] Embodiment 1: The existing multi-core optical fiber alignment method is easily affected by the external environment, and the alignment accuracy is not high. In order to solve the problems of the prior art, the embodiment 1 of the present invention provides a multi-core optical fiber alignment method based on the edge of the optical fiber core, such as Figure 1 As shown, including: Step 101: Acquire a first end face image of a first multi-core optical fiber and a second end face image of a second multi-core optical fiber.

[0032] The first end face image is an image corresponding to the surface to be fused of the first multi-core optical fiber, and the second end face image is an image corresponding to the surface to be fused of the second multi-core optical fiber, through which the two multi-core optical fibers are fused.

[0033] Step 102: filtering the first end surface image to obtain a first filtered image, and filtering the second end surface image to obtain a second filtered image.

[0034] The purpose of filtering the first end face image and the second end face image is to remove noise from the image and improve the image quality and clarity. The noise may come from factors such as imaging equipment and environmental interference, which will affect the subsequent extraction and alignment accuracy of the fiber core edge. The specific filtering method will be described below.

[0035] Step 103: Acquire a first fiber core edge image on the first filtered image, and acquire a second fiber core edge image on the second filtered image.

[0036] The first fiber core edge image and the second fiber core edge image are obtained in a similar manner. The first fiber core edge image on the first filter image can be obtained by an edge detection algorithm to obtain edge information in the image. Since the focus is on the edge of the fiber core, the influence of factors such as multi-core optical fiber end face damage and uneven brightness can be effectively eliminated. The specific acquisition method will be described below.

[0037] Step 104: Rotate the first fiber core edge image angle by angle, and perform cross-correlation calculation on the rotated first fiber core edge image and the second fiber core edge image respectively, until the best alignment angle corresponding to the maximum cross-correlation coefficient is obtained.

[0038] Among them, rotating by angle means first determining the rotation angle range (from 0° to 360°), starting from the minimum angle in the rotation angle range, and rotating by a preset angle each time until the rotation angle reaches the maximum angle in the rotation angle range. In one embodiment, the preset rotation angle range can be 0.1° to 0.5°. That is, the preset rotation angle can be 0.1° each time, or 0.5° each time, or any angle between 0.1° and 0.5° each time. The smaller the preset rotation angle, the higher the accuracy of the result obtained later.

[0039] For the first fiber core edge image, the image obtained after each rotation of the preset angle is cross-correlated with the second fiber core edge image. Each calculation will obtain a cross-correlation coefficient, and the rotation angle corresponding to the maximum cross-correlation coefficient is taken as the optimal alignment angle. Among them, this embodiment chooses to fix the second fiber core edge image and rotate the first fiber core edge image. It is also possible to keep the first fiber core edge image stationary and rotate the second fiber core edge image angle by angle. The calculation method is the same as above and will not be repeated here. The specific steps for cross-correlation calculation will be described below.

[0040] Step 105: Rotate the first multi-core optical fiber to the optimal alignment angle to align the first multi-core optical fiber with the second multi-core optical fiber.

[0041] In one embodiment, after the optimal alignment angle is obtained through the above steps, the optimal alignment angle is input into a fusion splicer, and the fusion splicer controls the first multi-core optical fiber to rotate to the optimal alignment angle to align the first multi-core optical fiber with the second multi-core optical fiber, and then performs a fusion splicing operation.

[0042] This embodiment obtains a multi-core optical fiber end face image, then processes the end face image to obtain a core edge image, and performs an edge cross-correlation algorithm on the core edge image, focusing mainly on the core edge image on the image rather than the overall brightness. Therefore, even if there are scratches, stains or other damages on the fusion end face of the multi-core optical fiber, the optical fiber can be accurately aligned, and the influence of factors such as optical fiber end face damage and uneven brightness can be effectively eliminated; on the other hand, the edge cross-correlation algorithm can effectively eliminate the correlation pseudo-peaks commonly seen in conventional cross-correlation algorithms and reduce misjudgments. The edge cross-correlation algorithm significantly reduces the occurrence of pseudo-peaks by extracting edge features of the image, thereby improving the accuracy of optical fiber alignment; by accurately aligning the multi-core optical fiber, perfect contact between the optical fiber end faces can be ensured, thereby reducing losses in signal transmission and extending the service life of the optical fiber system.

[0043] In one embodiment, Figure 2 As shown, the obtaining of a first end face image of the first multi-core optical fiber and a second end face image of the second multi-core optical fiber includes: Step 1011: strip the coating layer of the fusion end of the first multi-core optical fiber and the second multi-core optical fiber, cut the fusion end faces of the first multi-core optical fiber and the second multi-core optical fiber neatly, and respectively place the first multi-core optical fiber and the second multi-core optical fiber into the clamps at both ends of the fusion splicer and fix them.

[0044] For the first multi-core optical fiber and the second multi-core optical fiber, the coating layer at the fusion end needs to be stripped first. The coating layer is used to protect the optical fiber, but it will affect the fusion quality during fusion, so it needs to be removed. Then the fusion end face of the optical fiber is cut neatly, which helps to improve the accuracy and quality of the fusion and reduce the loss of the optical signal at the fusion. For example, a professional optical fiber cutter can be used for cutting to ensure that the end face is flat and burr-free.

[0045] Step 1012: Adjust the positions of the fusion end faces of the first multi-core optical fiber and the second multi-core optical fiber, and obtain a first intermediate image of the first multi-core optical fiber and a second intermediate image of the second multi-core optical fiber through end face imaging of a fusion splicer.

[0046] Place the processed first multi-core optical fiber and the second multi-core optical fiber into the clamps at both ends of the fusion splicer. The function of the clamp is to fix the optical fiber to ensure that the optical fiber does not move during the fusion splicing process. When fixing, ensure that the position of the optical fiber is accurate and firmly fixed in the clamp to prevent positional deviation in subsequent operations, which will affect the imaging quality and fusion splicing quality.

[0047] like Figure 3As shown, the end face imaging function of the fusion splicer is used to obtain a first intermediate image of the first multi-core optical fiber and a second intermediate image of the second multi-core optical fiber. End face imaging usually uses an optical system and an image sensor to photograph the end face of the optical fiber to obtain a clear image.

[0048] Step 1013: Perform grayscale conversion on the first intermediate image to obtain a first grayscale image, and perform grayscale conversion on the second intermediate image to obtain a second grayscale image.

[0049] The purpose of grayscale conversion is to simplify image information and reduce the amount of calculation for subsequent processing. Grayscale conversion can be achieved by calculating the color value of each pixel of a color image to obtain a grayscale value. For example, the weighted average method can be used to obtain the grayscale value by weighted averaging the values ​​of the red, green, and blue channels according to the sensitivity of the human eye to different colors.

[0050] In one embodiment, the grayscale conversion process includes: for each pixel in the color image (the first intermediate image and the second intermediate image), its color information is usually represented by the values ​​of the three channels of red (R), green (G), and blue (B). When performing grayscale conversion, different weights are assigned to the values ​​of the three channels, and then they are added to obtain the grayscale value of the pixel. Commonly used weights are 0.299, 0.587, and 0.114, and the calculation formula is Gray=0.299R+0.587G+0.114B. For example, for a pixel, its R=100, G=120, B=80, then its grayscale value Gray=0.299x100+ 0.587x120+0.114x80≈107.4. This weight distribution is based on the different sensitivities of the human eye to different colors. The human eye is most sensitive to green, followed by red, and least sensitive to blue.

[0051] According to the above method, the first intermediate image is converted from a color image to a first grayscale image, and the second intermediate image is converted from a color image to a second grayscale image.

[0052] Step 1014: Adjust the first grayscale image and the second grayscale image to the same size to obtain the first end surface image and the second end surface image.

[0053] After the grayscale image is obtained, in the subsequent image processing and analysis, since the two images need to be compared and calculated, if the image sizes are different, the processing difficulty and complexity will be increased. In one embodiment, the first grayscale image and the second grayscale image are adjusted to the same size in the following manner.

[0054] In one embodiment, Figure 4 As shown, the step 1014 specifically includes: Step 10141: Find the circular edge of the first grayscale image, obtain the first center coordinates and the first fiber pixel size of the first multi-core optical fiber; find the circular edge of the second grayscale image, obtain the second center coordinates and the second fiber pixel size of the second multi-core optical fiber.

[0055] Among them, Figure 5 As shown in the figure, since the end face of the optical fiber is usually circular, the position and size of the optical fiber can be determined by detecting the circular edge. The specific detection process includes: when detecting a circle, the equation of the circle is , where (a, b) is the coordinate of the center of the circle and r is the radius. The three parameters (a, b, r) constitute a three-dimensional space.

[0056] First, perform edge detection on the image to obtain a set of edge pixels. For each edge pixel (x, y), traverse the possible radius r (usually the radius value range is determined based on prior knowledge or a certain range), and then calculate the corresponding circle center coordinates (a, b). According to the circle equation:

[0057] The ± here means there are two possible solutions, and vote for the corresponding (a, b, r) combination in the parameter space.

[0058] For example, assuming that the coordinates of an edge pixel are (50, 50), and considering the radius r = 10, the possible coordinates of the center of the circle can be calculated, and then the counters of these possible (a, b, r) combinations in the parameter space are increased by 1, indicating that this combination has received one vote.

[0059] In the parameter space, after voting for all edge pixels, a three-dimensional "voting space" is formed. Detect the peak in this space, that is, the (a, b, r) combination with the most votes. Usually a threshold can be set, and when the number of votes exceeds this threshold, it is considered that a circle has been found. The setting of this threshold can be adjusted according to factors such as the noise situation of the image and the expected size of the circle. For example, if the threshold is set to 50, then the circle corresponding to the (a, b, r) combination with a vote number greater than 50 in the parameter space is considered to be the detected circle. After finding the (a, b, r) combination corresponding to the peak, the center coordinates (a, b) and radius r of the circle in the image are determined, thereby completing the detection of the circular edge.

[0060] Step 10142: cropping a first square image in which the first center coordinate is at the center of the first end face image using the first center coordinate and the first fiber pixel size; cropping a second square image in which the second center coordinate is at the center of the second end face image using the second center coordinate and the second fiber pixel size.

[0061] Once the circular edge is found, the center coordinates and pixel size of the optical fiber can be determined by calculating the center coordinates and radius of the circle. The specific method includes: for each pixel point on the circular edge, a circle can be fit using methods such as least squares method to obtain the center coordinates and radius of the circle.

[0062] The center coordinates may represent the location of the fiber in the image, while the radius may represent the pixel size of the fiber.

[0063] For example, suppose there are n pixels on the edge of the circle detected by the above method, and their coordinates are (x1, y1), (x2, y2)...(xn, yn). The least squares method can be used to fit a circle, and its equation is , where (a, b) is the center coordinate of the circle and r is the radius. By using the least squares method, the values ​​of a, b and r can be solved to obtain the center coordinate and pixel size of the optical fiber.

[0064] According to the first center coordinates of the first multi-core optical fiber and the first optical fiber pixel size, a first square image with the first center coordinates at the center of the first end face image can be cut out. The specific process includes: firstly determining the side length of the square image, which can be determined according to a certain multiple of the optical fiber pixel size. For example, the side length can be set to twice the optical fiber pixel size. Then, with the first center coordinate as the center, a square image with a side length of a determined value is cut out. For example, if the first optical fiber pixel size is d, the side length of the square image can be set to 2d. With the first center coordinates (x0, y0) as the center, a square image with a side length of 2d is cut out, and its upper left corner coordinates are (x0-d, y0-d), and the lower right corner coordinates are (x0+d, y0+d).

[0065] Similarly, according to the second center coordinates of the second multi-core optical fiber and the second optical fiber pixel size, a second square image with the second center coordinates at the center of the second end face image can be cropped. The cropping method is similar to that of the first square image, and will not be repeated here.

[0066] Step 10143: Adjust the sizes of the first square image and the second square image to be consistent, so as to obtain the first end surface image and the second end surface image.

[0067] Among them, Figure 6 As shown, the specific process of adjusting the sizes of the first square image and the second square image to be consistent includes: First, you need to determine the size of the image you want to resize. You can manually specify a fixed size, such as 500 pixels wide and 500 pixels high. Or you can determine the size of the two images and then find the average as the corresponding size. Using the size of one image as a reference, resize the other image to the same size.

[0068] For the first square image, use the resize function in the image adjustment tool to scale the image according to the specified target size. If the image is enlarged, new pixels will be inserted into the image through an interpolation algorithm; if the image is reduced, the pixels will be merged or discarded. For example, the first image is adjusted to a size of 500 pixels wide and 500 pixels high. After processing, the size of the first square image becomes the target size. The same operation is performed on the second square image. After processing, the second square image also reaches the target size. According to the above operation, the first end face image and the second end face image of the same size are finally obtained.

[0069] In one embodiment, in order to obtain the accuracy of the edge of the fiber core subsequently, it is necessary to remove the noise influence on the first end surface image and the second end surface image. In one embodiment, Figure 7 As shown, the filtering process of the first end face image and the second end face image is the same, and the filtering of the first end face image to obtain the first filtered image includes: performing logarithmic transformation and Fourier transformation on the first end face image to obtain a second intermediate image; filtering the second intermediate image through a filter, and performing inverse Fourier transformation on the filtered second intermediate image to obtain a third intermediate image; and performing exponential transformation on the third intermediate image to obtain the first filtered image.

[0070] Among them, the expression of the filter is:

[0071] Where n is the order of the filter, is the cutoff frequency of the filter, is the high frequency gain, is the low frequency gain, is the square root function, i and j are the filter coefficients.

[0072] The inverse Fourier transform formula is:

[0073] The exponential transformation formula is:

[0074] Where F is the Fourier transform, is the second intermediate image after filtering, is the third intermediate image, is an exponential function.

[0075] After obtaining the first filtered image and the second filtered image, Figure 8As shown, the acquiring of the first fiber core edge image on the first filtered image includes: Obtain the gradient amplitude and gradient direction of each pixel in the first filtered image; compare the gradient amplitude of each pixel with the adjacent pixel in its gradient direction, if the gradient amplitude of the current pixel is less than the gradient amplitude of any adjacent pixel, set the gradient amplitude of the current pixel to 0; if the gradient amplitude of the current pixel is greater than or equal to the gradient amplitudes of all adjacent pixels, retain the gradient amplitude corresponding to the current pixel; preset a high threshold and a low threshold, determine the pixel with a gradient amplitude greater than the high threshold as a strong edge pixel, determine the pixel with a gradient amplitude less than the low threshold as a non-edge pixel, and set the gradient amplitude to 0; for the pixel with a gradient amplitude between the high threshold and the low threshold, if the pixel is connected to the strong edge pixel, the pixel is determined to be a strong edge pixel; connect all the strong edge pixels together in sequence to obtain the first fiber core edge image.

[0076] Among them, the gradient amplitude reflects the intensity of the change of the pixel in the image. In image processing, for a two-dimensional image, the gradient amplitude of the pixel can be obtained by calculating the rate of change of the pixel in the horizontal and vertical directions. The larger the gradient amplitude, the more drastic the image change around the pixel, and it may be located at the edge. The gradient direction indicates the direction in which the pixel in the image changes the fastest. In a two-dimensional image, the gradient direction can be expressed as an angle, usually ranging from 0 to 360 degrees. The gradient direction is used to determine the direction of the edge.

[0077] For each pixel, compare its gradient amplitude with the gradient amplitude of the adjacent pixels in its gradient direction. If the gradient amplitude of the current pixel is less than the gradient amplitude of any adjacent pixel, it means that the pixel is unlikely to be a point on the edge, so its gradient amplitude is set to 0. For example, assuming that the gradient amplitude of the current pixel is 50 and the gradient amplitude of an adjacent pixel in its gradient direction is 60, then the gradient amplitude of the current pixel is set to 0. If the gradient amplitude of the current pixel is greater than or equal to the gradient amplitudes of all adjacent pixels, it means that the pixel is likely to be a prominent point on the edge, and the gradient amplitude corresponding to the current pixel is kept unchanged.

[0078] A high threshold and a low threshold are preset. The high threshold is usually set higher to determine obvious edge pixels. If the gradient amplitude of a pixel is greater than the high threshold, then the pixel is determined to be a strong edge pixel. Strong edge pixels are usually considered to be more reliable edge points. For pixels with a gradient amplitude less than the low threshold, they are determined to be non-edge pixels and their gradient amplitude is set to 0. These pixels are considered unlikely to be edge points and are excluded from the edge image.

[0079] For pixels whose gradient amplitude is between the high threshold and the low threshold, further judgment is required. If the pixel is connected to a determined strong edge pixel, then the pixel is also determined to be a strong edge pixel. The "connected" here can be judged in a variety of ways, for example, it can be checked whether the neighboring pixels of the pixel are strong edge pixels. If there are neighboring pixels that are strong edge pixels, then the pixel is considered to be connected to the strong edge pixel.

[0080] All pixels determined as strong edge pixels are connected together in a certain order. This order can be determined based on the positional relationship of the pixels, for example, they can be connected in order from top to bottom and from left to right. By connecting the strong edge pixels, a continuous edge contour is formed, and this contour is the first fiber core edge image. The fiber core edge image accurately reflects the edge position and shape of the fiber core in the first filter image, and provides an important basis for subsequent optical fiber alignment. Similarly, the second fiber core edge image can also be obtained in the above manner, which will not be repeated here.

[0081] After obtaining the first fiber core edge image and the second fiber core edge image, rotating the first fiber core edge image angle by angle, and performing cross-correlation calculation on the rotated first fiber core edge image and the second fiber core edge image respectively until obtaining the optimal alignment angle corresponding to the maximum value of the cross-correlation coefficient includes: First, determine the range of rotation, which is generally from 0 to 360 degrees. However, if there are certain physical limitations or a known approximate alignment range based on the actual situation of the optical fiber, this angle range can be narrowed. At the same time, determine the step size of the rotation, that is, the angle increment of each rotation. The smaller the step size, the higher the accuracy of the calculation may be, but the amount of calculation will also be greater. For example, the step size can be set to 0.1 or 0.5 degrees.

[0082] The first fiber core edge image is rotated according to the set step size, starting from the initial angle (usually 0 degrees). The rotation operation can be implemented by image processing software or related functions in programming languages. During the rotation process, the pixels of the image are rearranged according to the rotation matrix. For example, for a pixel (x, y) in the two-dimensional image (the first fiber core edge image), the rotation angle is When , its new coordinates (x', y') can be obtained by the matrix rotation formula, which is:

[0083] For the rotated first fiber core edge image A and the second fiber core edge image B, the cross-correlation calculation is to measure their similarity by calculating the sum of the products of the corresponding pixel points of A and B within a certain displacement range. The calculation formula of the cross-correlation coefficient is:

[0084] Where R is the mutual correlation coefficient, and are respectively the image matrices of the first core edge image and the image matrix of the second core edge image The number of rows and columns, yes The average value of yes The x-axis is the horizontal axis and the y-axis is the vertical axis.

[0085] The number of rows of the image matrix of the first fiber core edge image and the number of columns of the image matrix of the second fiber core edge image are both M, and the number of columns of the image matrix of the first fiber core edge image and the number of columns of the image matrix of the second fiber core edge image are both N. For each rotated first fiber core edge image, a cross-correlation calculation is performed between it and the second fiber core edge image. First, align the upper left corners of the two images, and then calculate the cross-correlation coefficient according to the above formula. Then, by moving one of the images (here is the rotated first fiber core edge image) in the horizontal and vertical directions, the cross-correlation coefficient can be repeatedly calculated within a certain displacement range. This displacement range can be determined according to the actual situation of the optical fiber and the size of the image.

[0086] After each rotation and cross-correlation calculation, record the obtained cross-correlation coefficient. The cross-correlation coefficient reflects the similarity of the two core edge images at the current rotation angle and displacement. After completing the rotation and cross-correlation calculation of all angles (within the set rotation range), compare all the recorded cross-correlation coefficients. Find the maximum value among them, and the rotation angle corresponding to this maximum value is the optimal alignment angle. Because at this angle, the similarity of the two core edge images is the highest, that is, the core alignment of the two optical fibers is the best. In one embodiment, as Fig. 9 As shown, the horizontal axis is the rotation angle, the vertical axis is the correlation coefficient, the left is the first fiber core edge image after rotation, and the right is the second fiber core edge image. It can be seen that when the rotation angle is 166.6°, the two images have the best correspondence.

[0087] After obtaining the optimal alignment angle through the above steps, the optimal alignment angle is input into the fusion splicer, and the fusion splicer controls the first multi-core optical fiber to rotate to the optimal alignment angle to align the first multi-core optical fiber with the second multi-core optical fiber, and then performs the fusion splicing operation. Fig.10 and Fig.11 As shown in the figure, the alignment results of seven-core optical fiber and four-core optical fiber are respectively shown, and the final fusion splicing effect is better.

[0088] Embodiment 2: In Example 1, a multi-core optical fiber alignment method based on the core edge is provided. In this embodiment, a multi-core optical fiber alignment device based on the core edge is proposed. The multi-core optical fiber alignment device based on the core edge includes: a processor and a memory for storing processor executable instructions; wherein the processor is configured to execute the multi-core optical fiber alignment method based on the core edge described in Example 1.

[0089] like Fig.12 As shown, the multi-core optical fiber alignment device based on the fiber core edge includes a processor 21 and a memory 22, wherein the processor 21 and the memory 22 can be connected via a bus or other means.

[0090] The processor 21 may be a central processing unit (CPU). The processor 21 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0091] The memory 22, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the multi-core optical fiber alignment method based on the fiber core edge in Embodiment 1 of the present invention. The processor executes various functional applications and training processes of the processor by running the non-transitory software programs, instructions and modules stored in the memory.

[0092] The memory 22 may include a program storage area and a training storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the training storage area may store training created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 22 may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The one or more modules are stored in the memory 22, and when executed by the processor 21, execution such as Figure 1The multi-core optical fiber alignment method based on the fiber core edge in the embodiment 1 is shown. The specific details of the multi-core optical fiber alignment method based on the fiber core edge can be referred to in Figure 1 , Figure 2 and Figure 3 The corresponding descriptions and effects in the embodiments shown in the figure can be understood and will not be repeated here.

[0093] This embodiment further provides a computer storage medium, which stores a computer program. The computer program can be executed by a processor to complete the multi-core optical fiber alignment method based on the core edge described in Example 1.

[0094] The computer storage medium stores computer executable instructions, and the computer executable instructions can execute the multi-core optical fiber alignment method based on the core edge in any of the above method embodiments. Wherein, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory. The specific steps of the multi-core optical fiber alignment method based on the core edge refer to Example 1, which will not be repeated in this embodiment.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-core optical fiber alignment method based on the edge of the fiber core, characterized in that: include: Acquire a first end face image of the first multi-core optical fiber and a second end face image of the second multi-core optical fiber; filtering the first end surface image to obtain a first filtered image, and filtering the second end surface image to obtain a second filtered image; Acquire a first fiber core edge image on the first filtered image, and acquire a second fiber core edge image on the second filtered image; Rotate the first fiber core edge image angle by angle, and perform cross-correlation calculation on the rotated first fiber core edge image and the second fiber core edge image respectively, until the best alignment angle corresponding to the maximum value of the cross-correlation coefficient is obtained; The first multi-core optical fiber is rotated to the optimal alignment angle to align the first multi-core optical fiber with the second multi-core optical fiber.

2. The multi-core optical fiber alignment method based on core edge according to claim 1, characterized in that: The obtaining of a first end face image of the first multi-core optical fiber and a second end face image of the second multi-core optical fiber comprises: Stripping the coating layers of the fusion ends of the first multi-core optical fiber and the second multi-core optical fiber, and neatly cutting the fusion end faces of the first multi-core optical fiber and the second multi-core optical fiber, respectively placing the first multi-core optical fiber and the second multi-core optical fiber into the clamps at both ends of the fusion splicer and fixing them; Adjusting the positions of the fusion-joined end faces of the first multi-core optical fiber and the second multi-core optical fiber, and obtaining a first intermediate image of the first multi-core optical fiber and a second intermediate image of the second multi-core optical fiber through end face imaging of a fusion-joining machine; Performing grayscale conversion on the first intermediate image to obtain a first grayscale image, and performing grayscale conversion on the second intermediate image to obtain a second grayscale image; The first grayscale image and the second grayscale image are adjusted to the same size to obtain the first end surface image and the second end surface image.

3. The multi-core optical fiber alignment method based on core edge according to claim 2, characterized in that: The step of adjusting the first grayscale image and the second grayscale image to the same size to obtain the first end surface image and the second end surface image comprises: Finding a circular edge of the first grayscale image, and obtaining a first center coordinate and a first optical fiber pixel size of the first multi-core optical fiber; Finding the circular edge of the second grayscale image, and obtaining the second center coordinates and the second optical fiber pixel size of the second multi-core optical fiber; A first square image with the first center coordinate at the center of the first end face image is cut out according to the first center coordinate and the first optical fiber pixel size; A second square image with the second center coordinate at the center of the second end face image is cut out according to the second center coordinate and the second optical fiber pixel size; The sizes of the first square image and the second square image are adjusted to be consistent, so as to obtain the first end surface image and the second end surface image.

4. The multi-core optical fiber alignment method based on core edge according to claim 1, characterized in that: The acquiring of a first fiber core edge image on the first filtered image comprises: Obtaining the gradient magnitude and gradient direction of each pixel in the first filtered image; Compare the gradient amplitude of each pixel with that of its adjacent pixels in the gradient direction. If the gradient amplitude of the current pixel is less than the gradient amplitude of any adjacent pixel, set the gradient amplitude of the current pixel to 0; if the gradient amplitude of the current pixel is greater than or equal to the gradient amplitudes of all adjacent pixels, retain the gradient amplitude corresponding to the current pixel. Preset a high threshold and a low threshold, determine the pixel points with a gradient amplitude greater than the high threshold as strong edge pixels, determine the pixel points with a gradient amplitude less than the low threshold as non-edge pixels, and set the gradient amplitude to 0; For a pixel point whose gradient amplitude is between the high threshold and the low threshold, if the pixel point is connected to the strong edge pixel point, the pixel point is determined as a strong edge pixel point; All the strong edge pixel points are connected together in sequence to obtain the first fiber core edge image.

5. The multi-core optical fiber alignment method based on core edge according to claim 1, characterized in that: The calculation formula of the mutual correlation coefficient is: Where R is the mutual correlation coefficient, and are respectively the image matrices of the first core edge image and the image matrix of the second core edge image The number of rows and columns, yes The average value of yes The x-axis is the horizontal axis and the y-axis is the vertical axis.

6. The multi-core optical fiber alignment method based on core edge according to claim 1, characterized in that: The filtering process of the first end face image and the second end face image is the same, and filtering the first end face image to obtain the first filtered image includes: Performing logarithmic transformation and Fourier transformation on the first end surface image to obtain a second intermediate image; filtering the second intermediate image by a filter, and performing an inverse Fourier transform on the filtered second intermediate image to obtain a third intermediate image; Perform an exponential transformation on the third intermediate image to obtain the first filtered image.

7. The multi-core optical fiber alignment method based on core edge according to claim 6, characterized in that: The expression of the filter is: Where n is the order of the filter, is the cutoff frequency of the filter, is the high frequency gain, is the low frequency gain, is the square root function, i and j are the filter coefficients.

8. The multi-core optical fiber alignment method based on core edge according to claim 6, characterized in that: The inverse Fourier transform formula is: The exponential transformation formula is: Where F is the Fourier transform, is the second intermediate image after filtering, is the third intermediate image, is an exponential function.

9. A multi-core optical fiber alignment device based on the edge of the fiber core, characterized in that: The multi-core optical fiber alignment device based on the fiber core edge includes: a processor and a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the multi-core optical fiber alignment method based on core edge as described in any one of claims 1-8.

10. A non-volatile computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, and the computer executable instructions are executed by one or more processors to complete the multi-core optical fiber alignment method based on core edges as described in any one of claims 1-8.

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