Beacon light centroid positioning method, device and equipment in optical communication based on improved wavelet threshold

Abstract

The invention discloses a beacon light centroid positioning method, device and equipment in optical communication based on an improved wavelet threshold, and relates to the technical field of optical communication, and the method comprises the steps: obtaining a plurality of beacon light images; carrying out median filtering processing and discrete wavelet transformation on each image to obtain a plurality of wavelet decomposition multilayer coefficient matrixes, and obtaining a denoising matrix by using a VisuShrink method; performing discrete wavelet inverse transformation on the denoising matrix to obtain a plurality of denoised light spot images; calculating a centroid coordinate of the light spot in each de-noised light spot image by adopting a gray centroid method; and carrying out summation and averaging processing on the centroid coordinates of the plurality of images to obtain beacon light centroid positioning coordinates. By improving wavelet threshold processing, noise interference is effectively removed, the accuracy and stability of beacon light centroid positioning are improved, and reliable technical support is provided for accurate positioning in optical communication.

Description

Technical Field

[0001] The present application relates to the field of optical communication technology, and in particular to a method, device and equipment for locating the centroid of a beacon in optical communication based on an improved wavelet threshold. Background Art

[0002] With the rapid development of aerospace technology and increasingly stringent requirements for the speed, distance, and stability of space communications, satellite optical communications, with their significant advantages such as high speed, ultra-long distance, low power consumption, and strong anti-interference, have become the core technical support for building an integrated space-ground information network. As a key signal for the alignment of satellite optical communication links, the center of mass positioning accuracy of beacon light directly determines whether communication terminals can achieve micro-radian dynamic alignment in complex space environments. It is a key technical indicator for ensuring the stable and reliable operation of satellite optical communication systems.

[0003] However, in actual operation, satellite optical communication systems face multiple sources of combined interference, including pointing deviations caused by micro-vibration orbital maneuvers of satellite platforms, light spot jitter and expansion distortion caused by atmospheric turbulence, and strong interference from space background light. In related technologies, the beacon light centroid positioning method mainly relies on the direct output of the light spot grayscale distribution by the photoelectric detector and the calculation of the position coordinates through the weighted centroid algorithm. However, in the above-mentioned composite interference scenario, the related technology has the following obvious shortcomings: the light spot image is seriously polluted by noise, resulting in a large deviation in the calculation of the centroid, and it is impossible to effectively distinguish between valid signals and interference noise. Under dynamic disturbances, it is difficult to take into account both millisecond-level response speed and sub-micro-radian positioning accuracy, which in turn causes frequent interruptions of the laser link and excessively long link recovery time.

[0004] Therefore, this application proposes a beacon light centroid positioning method in optical communication based on improved wavelet threshold to solve the above problems, improve the accuracy and stability of beacon light centroid positioning, and ensure the efficient operation of satellite optical communication systems. Summary of the Invention

[0005] In order to improve the spot distortion of beacon light in satellite optical communication links caused by atmospheric turbulence, achieve stable operation of optical communication, and enable fast, accurate connection and stable maintenance of laser links, based on the unique advantages of wavelet threshold denoising method for image denoising, the purpose of this application is to provide a beacon light centroid positioning method, device and equipment in optical communication based on improved wavelet threshold, which can quickly identify the centroid coordinates of the light spot in the beacon light image, thereby ensuring the stability and reliability of the optical communication laser link.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for locating the centroid of a beacon in optical communication based on an improved wavelet threshold, comprising:

[0008] Acquire multiple beacon light images;

[0009] Perform median filtering and discrete wavelet transform on each beacon light image to obtain a wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image;

[0010] Each wavelet decomposition multi-layer coefficient matrix is denoised by the VisuShrink method to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices;

[0011] Perform discrete wavelet inverse transform on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised spot images;

[0012] The grayscale centroid method is used to calculate the centroid coordinates of the light spot in each denoised light spot image;

[0013] The centroid coordinates of the light spots in multiple denoised light spot images are summed and averaged to obtain the beacon light centroid positioning coordinates.

[0014] In a second aspect, the present application provides a beacon light centroid positioning device in optical communication based on an improved wavelet threshold, comprising:

[0015] An image acquisition module, used to acquire multiple beacon light images;

[0016] The filtering and wavelet decomposition module is used to perform median filtering and discrete wavelet transform on each beacon light image to obtain a wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image;

[0017] A denoising processing module is used to denoise each wavelet decomposition multi-layer coefficient matrix by using the VisuShrink method to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices;

[0018] The discrete wavelet inverse transform module is used to perform discrete wavelet inverse transform on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised spot images;

[0019] The light spot centroid calculation module is used to calculate the centroid coordinates of the light spot in each denoised light spot image using the grayscale centroid method;

[0020] The centroid coordinate summing and averaging module is used to sum and average the centroid coordinates of the light spots in multiple denoised light spot images to obtain the beacon light centroid positioning coordinates.

[0021] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described steps of the method for locating the centroid of beacon light in optical communication based on improved wavelet threshold.

[0022] According to the specific embodiments provided in this application, this application has the following technical effects:

[0023] The present application provides a beacon light centroid positioning method, device and equipment in optical communication based on improved wavelet threshold, which obtains multiple beacon light images and performs median filtering and discrete wavelet transform on each beacon light image respectively to obtain a wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image, thereby solving the problem of mixing of noise and spot signal in the beacon light image, and realizing effective separation of noise and spot signal in the frequency domain while retaining the multi-scale feature information of the spot; denoising is performed on each wavelet decomposition multi-layer coefficient matrix respectively by the VisuShrink method to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices, thereby solving the problem of traditional denoising methods for falsely suppressing weak spot signals in the beacon light image, and realizing maximum retention of the integrity of the spot signal while removing noise; The multi-layer coefficient denoising matrix is solved and the inverse discrete wavelet transform is performed to obtain multiple denoised light spot images, which solves the problem of light spot shape and position distortion in the image reconstruction process after denoising, realizes high-quality light spot image reconstruction, and provides an accurate image basis for subsequent centroid calculation; by using the grayscale centroid method to calculate the centroid coordinates of the light spot in each denoised light spot image, the problem of poor adaptability to the irregularity of the light spot shape in the centroid calculation is solved, and high-precision centroid positioning of light spots of arbitrary shapes is achieved; by summing and averaging the centroid coordinates of the light spots in multiple denoised light spot images, the beacon light centroid positioning coordinates are obtained, which solves the problem of insufficient reliability of single image centroid positioning and the difficulty of fusing centroid information of multiple images, realizes the optimal integration of centroid information of multiple images, and improves the stability and accuracy of beacon light centroid positioning. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0025] Figure 1 This is a diagram of an application environment of a beacon light centroid method in optical communication based on an improved wavelet threshold in an embodiment of the present application;

[0026] Figure 2 A flowchart of a method for determining the centroid of beacon light in optical communication based on an improved wavelet threshold provided in one embodiment of the present application;

[0027] Figure 3 A schematic diagram of the functional modules of a beacon optical centroid device in optical communication based on an improved wavelet threshold provided in one embodiment of the present application;

[0028] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0030] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0031] The beacon light centroid method in optical communication based on improved wavelet threshold provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send multiple beacon light images to the server 104. After the server 104 receives the multiple beacon light images, the server 104 performs median filtering and discrete wavelet transform on each beacon light image to obtain a wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image; each wavelet decomposition multi-layer coefficient matrix is denoised by the VisuShrink method to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices; each wavelet decomposition multi-layer coefficient denoising matrix is subjected to discrete wavelet inverse transform to obtain multiple denoised light spot images; the grayscale centroid method is used to calculate the centroid coordinates of the light spot in each denoised light spot image; the centroid coordinates of the light spots in the multiple denoised light spot images are summed and averaged to obtain the beacon light centroid positioning coordinates. The server 104 can feed back the beacon light centroid positioning coordinates to the terminal 102. In addition, in some embodiments, the beacon light centroid positioning method in optical communication based on the improved wavelet threshold can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly locate the beacon light centroid based on the beacon light image, or the server 104 can obtain the beacon light image from the data storage system and locate the beacon light centroid based on the beacon light image.

[0032] The terminal 102 may be, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. Portable wearable devices may include smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0033] In an exemplary embodiment, Figure 2 As shown, a method for beacon light centroid in optical communication based on improved wavelet threshold is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps 201 to 206.

[0034] Step 201: Acquire multiple beacon light images. A CMOS camera may be used to acquire the beacon light images, and the acquired beacon light images are sequentially stored.

[0035] Step 202 : Perform median filtering and discrete wavelet transform on each beacon light image to obtain a wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image.

[0036] Step 203 , denoising each wavelet decomposition multi-layer coefficient matrix by using the VisuShrink method to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices.

[0037] Step 204 : Perform discrete wavelet inverse transform on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised light spot images.

[0038] Step 205 : Using the grayscale centroid method, calculate the centroid coordinates of the light spot in each denoised light spot image.

[0039] Step 206: Sum and average the centroid coordinates of the light spots in the multiple denoised light spot images to obtain the beacon light centroid positioning coordinates. The abscissa and ordinate coordinates of all the obtained centroid coordinates are summed to obtain the sum of the centroid abscissa and ordinate. The sum of the centroid abscissa and ordinate is then divided by the number of denoised light spot images to obtain the beacon light centroid positioning coordinates.

[0040] By implementing the above steps 201 to 206, the present application can effectively improve the accuracy and reliability of beacon light centroid positioning in optical communications.

[0041] In another exemplary embodiment of the present application, the collected beacon light images are sequentially processed, and the spot image is first subjected to median filtering to effectively suppress random noise, providing a favorable basis for the subsequent precise positioning of the spot centroid. In order to further optimize the feature extraction and noise suppression effects of the beacon light image and improve the accuracy and adaptability of the wavelet decomposition, the above step 202 specifically includes:

[0042] The median-filtered image is subjected to a discrete wavelet transform using the db7 wavelet basis and three decomposition layers. The selection of the wavelet basis and number of decomposition layers was determined through simulation analysis. This yields the optimal MSE (Mean Squared Error), PSNIR (Peak Signal-to-Noise Ratio), and energy loss for the processed image. Specifically, the following formula is used to perform a discrete wavelet transform on each median-filtered beacon light image, yielding multiple intermediate matrices:

[0043]

[0044] Among them, x m,n represents the wavelet coefficient of the mth row and nth column in the intermediate matrix; R represents the integration area; f(t) represents the beacon light image after median filtering at time t; <,> represents the inner product operation; j represents the scaling parameter of the wavelet function; k represents the translation parameter of the wavelet transform; ψ j,k (t) represents the wavelet function after the transformation of the scaling parameter j and the translation parameter k at time t; Represents ψ m,n (t); a0 represents the initial scaling factor; b0 represents the initial translation factor.

[0045] Use the following formula to perform trigonometric function transformation on each intermediate matrix to obtain multiple wavelet decomposition multi-layer coefficient matrices:

[0046]

[0047] Among them, g() represents the change function; c max Indicates the maximum value of the wavelet coefficient; c min represents the minimum value of the wavelet coefficients; α represents the adjustment parameter. By adjusting the value of α, the variation of the wavelet coefficients near the threshold can be changed, thereby more effectively separating the noise coefficients from the useful coefficients and improving the denoising performance. Simulation analysis shows that the denoising effect is optimal when α is 1.

[0048] In another exemplary embodiment of the present application, in order to further improve the accuracy and adaptability of beacon light image denoising, optimize the denoising effect and better preserve the characteristics of the light spot signal, the above step 203 is replaced by the following steps 301 to 303:

[0049] Step 301: Determine the coefficient threshold of each wavelet decomposition coefficient transformation matrix by using the VisuShrink method. J , LH J 、HH J The wavelet coefficients (J = 1, 2, 3, indicating the number of wavelet decomposition layers) are calculated by the VisuShrink method to calculate the J-th level coefficient HL J , vertical coefficient LH J , diagonal coefficient HH J The corresponding threshold value is obtained. At the same time, the vertical coefficient LH3 of the third layer is used to calculate the threshold value of the low-frequency wavelet coefficient. The threshold function is the wavelet threshold denoising of the proposed new comprehensive function.

[0050] As an optional implementation, step 301 specifically includes: using the following formula to determine the coefficient threshold of each wavelet decomposition coefficient transformation matrix:

[0051]

[0052] Where T is the coefficient threshold for the Jth layer; σ is the noise standard deviation; M and N are the pixels in the Mth row and Nth column of the median-filtered beacon light image, respectively; s = [1, 2, 3]; s = 1, s = 2, and s = 3 correspond to the high-frequency subband coefficients in the horizontal, vertical, and diagonal directions, respectively; and J represents the number of wavelet decomposition layers. This threshold calculation method primarily improves upon the global threshold by applying the wavelet coefficients of all layers and components to fully utilize the wavelet data while improving the denoising effect.

[0053] Step 302: Based on the coefficient threshold of each wavelet decomposition coefficient transformation matrix, a preset threshold function is used to perform wavelet denoising.

[0054] As an optional implementation, the coefficient threshold calculated in the previous step is substituted into the newly proposed preset threshold function, and the wavelet coefficient is substituted into the preset threshold function to obtain the wavelet coefficient after wavelet threshold denoising. The preset threshold function in step 302 is:

[0055]

[0056] in, represents the wavelet coefficient of the mth row and nth column in the intermediate matrix after denoising; x m,nRepresents the wavelet coefficient of the mth row and nth column in the intermediate matrix; m' and n' respectively represent the first constant and the second constant, which are generally positive numbers and are used to control the transformation of the wavelet coefficient when it is greater than the threshold. In order to ensure continuity, n = (1-T) (1+e -m The preset threshold function first has continuity to ensure the denoising effect of the method and good visual effects; it combines the advantages and disadvantages of the two classic methods of soft threshold and hard threshold, takes advantage of their strengths and avoids their weaknesses, avoids the damage of effective information while filtering out the noise coefficient as much as possible.

[0057] Step 303: Perform inverse trigonometric function transformation on the wavelet coefficients after the wavelet denoising process to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices.

[0058] As an optional implementation, the expression for the inverse trigonometric function transformation in step 303 is:

[0059]

[0060] Where g′() represents the inverse transformation trigonometric function; c max Indicates the maximum value of the wavelet coefficient; c min represents the minimum value of the wavelet coefficient; α represents the adjustment parameter.

[0061] In another exemplary embodiment of the present application, the inverse discrete wavelet transform in step 204 is:

[0062]

[0063] Where f′(t) represents the denoised spot image at time t; j represents the scaling parameter of the wavelet function; k represents the translation parameter of the wavelet transform; ψ j,k (t) represents the wavelet function after the transformation of the scaling parameter j and the translation parameter k at the tth moment.

[0064] At this point, the denoised spot image is obtained, completing the entire denoising process. This application performs median filtering and wavelet threshold denoising using a preset threshold function on the noisy image, effectively removing noise from the image and suppressing the effects of atmospheric turbulence and background light on the beacon light spot. Furthermore, the proposed preset threshold function is simple, ensuring the overall real-time performance of the solution and enabling rapid positioning.

[0065] In another exemplary embodiment of the present application, step 205 specifically includes:

[0066]

[0067] Among them, x c and y cwhere i and j′ represent the grayscale level of the pixel in the denoised spot image, i and j′ represent the grayscale level of the pixel in the ith row and j′th column, respectively. f(i, j') represents the frequency of the grayscale level in the ith row and j′th column, and S is the total number of pixels in the denoised spot image. Due to the low complexity of each algorithm, the runtime is short, the denoising effect is excellent, and positioning accuracy can be improved, achieving rapid positioning.

[0068] This application aims at denoising the image of beacon light in a laser link affected by atmospheric turbulence to ensure the rapid, continuous and stable operation of the laser link. It has good denoising effect, high precision, fast calculation speed, can achieve sub-pixel positioning accuracy, and is real-time, thereby better ensuring the stability and reliability of the laser communication link.

[0069] The present application also provides an application scenario, which applies the above-mentioned beacon light centroid method in optical communication based on improved wavelet threshold. Specifically: the beacon light centroid method in optical communication based on improved wavelet threshold provided in this embodiment can be applied to the light beam alignment and tracking scenario in the optical communication system. The light beam alignment and tracking scenario includes an optical signal emission link, an optical signal transmission link, and an optical signal reception and processing link. The optical signal enters the optical signal transmission link from the optical signal emission link, passes through various interferences in the optical signal transmission process (such as atmospheric turbulence, noise, etc.), and finally reaches the optical signal reception and processing link. The beacon light centroid positioning method in optical communication based on improved wavelet threshold provided in this embodiment belongs to the light spot centroid positioning link in the optical signal reception and processing link. Specifically, in the optical signal reception and processing phase, the beacon light in the optical signal can be processed based on a denoising and centroid calculation method using an improved wavelet threshold. This involves acquiring multiple beacon light images, filtering, wavelet transforming, denoising, inverse transforming, centroid calculation, and summing and averaging. This significantly improves the accuracy and reliability of centroid positioning, providing a more accurate basis for beam alignment and tracking. By optimizing the denoising and centroid calculation processes, this method can effectively address various interferences encountered during optical signal transmission, ensuring the efficient operation of optical communication systems.

[0070] Based on the same inventive concept, the embodiment of the present application also provides a device for realizing the above-mentioned method for realizing the beacon light centroid in optical communication based on an improved wavelet threshold. The solution provided by the device for solving the problem is similar to the solution described in the above-mentioned method. Therefore, the specific limitations of the embodiments of one or more devices for realizing the beacon light centroid in optical communication based on an improved wavelet threshold provided below can be found in the above-mentioned limitations of the method for realizing the beacon light centroid in optical communication based on an improved wavelet threshold, and will not be repeated here.

[0071] In an exemplary embodiment, Figure 3 As shown, a beacon optical centroid device in optical communication based on an improved wavelet threshold is provided, comprising:

[0072] The image acquisition module 401 is used to acquire multiple beacon light images.

[0073] The filtering and wavelet decomposition module 402 is used to perform median filtering and discrete wavelet transform on each beacon light image to obtain a wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image.

[0074] The denoising processing module 403 is used to denoise each wavelet decomposition multi-layer coefficient matrix using the VisuShrink method to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices.

[0075] The discrete wavelet inverse transform module 404 is used to perform discrete wavelet inverse transform on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised spot images.

[0076] The light spot centroid calculation module 405 is used to calculate the centroid coordinates of the light spot in each denoised light spot image using a grayscale centroid method.

[0077] The centroid coordinate summing and averaging module 406 is used to sum and average the centroid coordinates of the light spots in the multiple denoised light spot images to obtain the beacon light centroid positioning coordinates.

[0078] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store beacon light image processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a beacon light centroid method in optical communication based on an improved wavelet threshold is implemented.

[0079] Those skilled in the art will understand that Figure 4The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0080] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0081] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0082] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0083] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0084] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0085] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for locating the centroid of beacon light in optical communication based on improved wavelet threshold, characterized in that: The method for locating the beacon light centroid in optical communication based on the improved wavelet threshold comprises: Acquire multiple beacon light images; Perform median filtering and discrete wavelet transform on each beacon light image to obtain a wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image; Each wavelet decomposition multi-layer coefficient matrix is denoised by the VisuShrink method to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices; Perform discrete wavelet inverse transform on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised spot images; The grayscale centroid method is used to calculate the centroid coordinates of the light spot in each denoised light spot image; The centroid coordinates of the light spots in multiple denoised light spot images are summed and averaged to obtain the beacon light centroid positioning coordinates.

2. The method for locating the centroid of beacon light in optical communication based on improved wavelet threshold according to claim 1, characterized in that: The median filtering and discrete wavelet transform are performed on each beacon light image to obtain a wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image, specifically including: Use the following formula to perform discrete wavelet transform on each beacon light image after median filtering to obtain multiple intermediate matrices: Among them, x m,n represents the wavelet coefficient of the mth row and nth column in the intermediate matrix; R represents the integration area; f(t) represents the beacon light image after median filtering at time t; <,> represents the inner product operation; j represents the scaling parameter of the wavelet function; k represents the translation parameter of the wavelet transform; ψ j,k (t) represents the wavelet function after the transformation of the scaling parameter j and the translation parameter k at time t; Represents ψ j,k The complex conjugate of (t); a0 represents the initial scaling factor; b0 represents the initial translation factor; Use the following formula to perform trigonometric function transformation on each intermediate matrix to obtain multiple wavelet decomposition multi-layer coefficient matrices: Among them, g() represents the change function; c max Indicates the maximum value of the wavelet coefficient; c min represents the minimum value of the wavelet coefficient; α represents the adjustment parameter.

3. The method for locating the centroid of beacon light in optical communication based on improved wavelet threshold according to claim 1, characterized in that: The denoising of each wavelet decomposition multi-layer coefficient matrix is performed by the VisuShrink method to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices, specifically including: By using the VisuShrink method, the coefficient threshold of each wavelet decomposition coefficient transformation matrix is determined; Based on the coefficient threshold of each wavelet decomposition coefficient transformation matrix, a preset threshold function is used to perform wavelet denoising; The wavelet coefficients after the wavelet denoising process are subjected to an inverse trigonometric function transformation to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices.

4. The method for locating the centroid of beacon light in optical communication based on improved wavelet threshold according to claim 3, characterized in that: The method of determining the coefficient threshold of each wavelet decomposition coefficient transformation matrix by the VisuShrink method specifically includes: Use the following formula to determine the coefficient threshold of each wavelet decomposition coefficient transformation matrix: Among them, T J is the coefficient threshold of the Jth wavelet decomposition layer; σ is the noise standard deviation; M and N are the Mth row pixel and the Nth column pixel of the median filtered beacon light image, respectively; s = [1, 2, 3]; s = 1, s = 2 and s = 3 correspond to the high-frequency sub-band coefficients in the horizontal direction, the high-frequency sub-band coefficients in the vertical direction and the high-frequency sub-band coefficients in the diagonal direction, respectively.

5. The method for locating the centroid of beacon light in optical communication based on improved wavelet threshold according to claim 3, characterized in that: The preset threshold function is: in, represents the wavelet coefficient of the mth row and nth column in the intermediate matrix after denoising; x m,n represents the wavelet coefficient of the mth row and nth column in the intermediate matrix; m′ and n′ represent the first constant and the second constant respectively.

6. The method for locating the centroid of beacon light in optical communication based on improved wavelet threshold according to claim 3, characterized in that: The expression of the inverse transformation of the trigonometric function is: Where g′() represents the inverse transformation trigonometric function; c max Indicates the maximum value of the wavelet coefficient; c min represents the minimum value of the wavelet coefficient; α represents the adjustment parameter.

7. The method for locating the centroid of beacon light in optical communication based on improved wavelet threshold according to claim 1, characterized in that: The inverse discrete wavelet transform is: Where f′(t) represents the denoised spot image at time t; j represents the scaling parameter of the wavelet function; k represents the translation parameter of the wavelet transform; ψ j,k (t) represents the wavelet function after the transformation of the scaling parameter j and the translation parameter k at the tth moment.

8. The method for locating the centroid of beacon light in optical communication based on improved wavelet threshold according to claim 1, characterized in that: The grayscale centroid method is used to calculate the centroid coordinates of the light spot in each denoised light spot image, specifically including: Among them, x c and y c They represent the horizontal and vertical coordinates of the center of mass of the light spot in the denoised light spot image, respectively. i and j′ represent the grayscale level of the pixel in the i-th row and the j′th column of the denoised light spot image, respectively. f(i,j') represents the frequency of the grayscale level in the i-th row and j′th column. S is the total number of pixels in the denoised light spot image.

9. A beacon light centroid positioning device in optical communication based on improved wavelet threshold, characterized in that: The device for locating the beacon light centroid in optical communication based on the improved wavelet threshold comprises: An image acquisition module, used to acquire multiple beacon light images; The filtering and wavelet decomposition module is used to perform median filtering and discrete wavelet transform on each beacon light image to obtain a wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image; A denoising processing module is used to denoise each wavelet decomposition multi-layer coefficient matrix by using the VisuShrink method to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices; The discrete wavelet inverse transform module is used to perform discrete wavelet inverse transform on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised spot images; The light spot centroid calculation module is used to calculate the centroid coordinates of the light spot in each denoised light spot image using the grayscale centroid method; The centroid coordinate summing and averaging module is used to sum and average the centroid coordinates of the light spots in multiple denoised light spot images to obtain the beacon light centroid positioning coordinates.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the beacon light centroid positioning method in optical communication based on improved wavelet threshold according to any one of claims 1 to 8.

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