WO2024016632A1 - Bright spot location method, bright spot location apparatus, electronic device and storage medium - Google Patents

Abstract

The present invention belongs to the technical field of location. Provided in the embodiments of the present invention are a bright spot location method, a bright spot location apparatus, an electronic device and a storage medium. The method comprises: acquiring an original grayscale image to be processed; performing image preprocessing on the original grayscale image to obtain an initial background image, and performing ratio calculation on the original grayscale image and the initial background image, so as to obtain a signal-to-noise ratio matrix; performing binarization segmentation on the signal-to-noise ratio matrix to obtain a binarized image; according to the binarized image, performing image equalization processing on the original grayscale image, so as to obtain an initial grayscale image; performing image filtering processing on the initial grayscale image to obtain a target grayscale image; according to the binarized image, performing bright spot detection on the target grayscale image, so as to obtain a target bright spot; and by means of a preset algorithm, performing location processing on the target bright spot, so as to obtain position data of and light intensity data of the target bright spot. The embodiments of the present invention can improve the accuracy of bright spot location.

Description

Highlight positioning method, highlight positioning device, electronic equipment and storage medium

This invention requires the priority of the Chinese patent application submitted to the China Patent Office on July 22, 2022, with the application number 202210870876.6, and the application name is "Bright spot positioning method, bright spot positioning device, electronic equipment and storage medium", and its entire content has passed This reference is incorporated herein by reference.

Technical field

The present invention relates to the field of positioning technology, and in particular to a bright spot positioning method, a bright spot positioning device, electronic equipment and a storage medium.

Background technique

Current bright spot positioning methods often have problems such as inaccurate background estimation and uneven bright spot segmentation during image processing, which affects the accuracy of bright spot positioning. Therefore, how to improve the accuracy of bright spot positioning has become an urgent technical problem to be solved.

Contents of the invention

The main purpose of the embodiments of the present invention is to propose a bright spot positioning method, a bright spot positioning device, electronic equipment and a storage medium, aiming to improve the accuracy of bright spot positioning.

In order to achieve the above object, a first aspect of the embodiment of the present invention proposes a bright spot positioning method, which method includes:

Get the original grayscale image to be processed;

Perform image preprocessing on the original grayscale image to obtain an initial background image;

Perform ratio calculation on the original grayscale image and the initial background image to obtain a signal-to-noise ratio matrix;

Perform binary segmentation on the signal-to-noise ratio matrix to obtain a binary image;

Perform image equalization processing on the original grayscale image according to the binary image to obtain an initial grayscale image;

Perform image filtering processing on the initial grayscale image to obtain a target grayscale image;

Perform bright spot detection on the target grayscale image according to the binary image to obtain the target bright spot;

The target bright spot is positioned through a preset algorithm to obtain the position data and light intensity data of the target bright spot.

In some embodiments, the step of performing image preprocessing on the original grayscale image to obtain an initial background image includes:

Perform histogram statistics on the original grayscale image within a preset interval to obtain a brightness histogram;

Perform feature extraction on the brightness histogram to obtain local background values;

Perform background subtraction processing on the original grayscale image according to the local background value to obtain the initial background image and an initial background-removed image.

In some embodiments, the step of performing binary segmentation on the signal-to-noise ratio matrix to obtain a binary image includes:

Perform threshold calculation according to the signal-to-noise ratio matrix and the brightness histogram to obtain a binarized threshold;

Binarize and segment the signal-to-noise ratio matrix according to the binarization threshold to obtain the binarized image.

In some embodiments, the step of performing image equalization processing on the original grayscale image according to the binary image to obtain an initial grayscale image includes:

Perform brightness calculation on the original grayscale image according to the binarized image to obtain the mean foreground brightness of the binarized image;

The original grayscale image is brightness normalized according to the foreground brightness mean value to obtain the initial grayscale image.

In some embodiments, the step of performing image filtering on the initial grayscale image to obtain a target grayscale image includes:

Perform Gaussian filtering on the initial grayscale image to obtain a first filtered image;

Perform Laplacian sharpening processing on the first filtered image to obtain the target grayscale image.

In some embodiments, the step of performing bright spot detection on the target grayscale image according to the binary image to obtain the target bright spot includes:

Perform highlight detection on the target grayscale image according to the binarized image to obtain the mean foreground brightness of the binarized image;

Determine an intensity threshold for bright spot detection based on the average foreground brightness;

Construct a bright spot connected graph according to the adjacency relationship between candidate bright spots in the target grayscale image;

Filter the candidate bright spots of the bright spot connected graph according to the brightness values of the candidate bright spots to obtain initial bright spots;

The initial bright spots are detected according to preset filtering conditions to obtain the target bright spots.

In some embodiments, the position data includes sub-pixel center coordinates, the light intensity data includes light intensity values, and the target bright spot is positioned through a preset algorithm to obtain the position data and light intensity of the target bright spot. Steps to strong data include:

Perform coordinate identification on the target bright spot through the center of gravity method to obtain the sub-pixel center coordinates of the target bright spot;

The brightness of the sub-pixel center coordinates is extracted using a quadratic spline interpolation method to obtain the light intensity value of the target bright spot.

In order to achieve the above object, a second aspect of the embodiment of the present invention proposes a bright spot positioning device, the device includes:

Image acquisition module, used to acquire the original grayscale image to be processed;

An image preprocessing module, used to perform image preprocessing on the original grayscale image to obtain an initial background image;

A calculation module for performing ratio calculation on the original grayscale image and the initial background image to obtain a signal-to-noise ratio matrix;

An image segmentation module, used to perform binary segmentation on the signal-to-noise ratio matrix to obtain a binary image;

An image equalization module, configured to perform image equalization processing on the original grayscale image according to the binary image to obtain an initial grayscale image;

An image filtering module, used to perform image filtering processing on the initial grayscale image to obtain a target grayscale image;

A bright spot detection module, configured to perform bright spot detection on the target grayscale image based on the binary image to obtain the target bright spot;

A positioning module is used to perform positioning processing on the target bright spot through a preset algorithm to obtain position data and light intensity data of the target bright spot.

In order to achieve the above object, a third aspect of the embodiment of the present invention proposes an electronic device. The electronic device includes a memory, a processor, a program stored on the memory and executable on the processor, and a program for A data bus that implements connection and communication between the processor and the memory. When the program is executed by the processor, the method described in the first aspect is implemented.

In order to achieve the above object, the fourth aspect of the embodiment of the present invention proposes a storage medium. The storage medium is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs. The one or more programs may be executed by one or more processors to implement the method described in the first aspect above.

The bright spot positioning method, bright spot positioning device, electronic equipment and storage medium proposed by the present invention obtain an original grayscale image to be processed; perform image preprocessing on the original grayscale image to obtain an initial background image; and perform image processing on the original grayscale image and The initial background image is ratio calculated to obtain a signal-to-noise ratio matrix, and the signal-to-noise ratio matrix is binarized and segmented to obtain a binarized image, which can improve the accuracy of image background estimation and image segmentation. Further, image equalization processing is performed on the original grayscale image according to the binary image to obtain an initial grayscale image, which can globally equalize the initial grayscale image. At the same time, by performing image filtering processing on the initial grayscale image, the target grayscale image is obtained. degree image, which can improve the quality of the target image. Finally, perform highlight detection on the target grayscale image based on the binary image to obtain the target highlight; perform positioning processing on the target highlight through a preset algorithm to obtain the position data and light intensity data of the target highlight. This method can more accurately identify the target highlight. Identify the target bright spots and determine the location of the target bright spots, thereby improving the accuracy of bright spot positioning.

Description of drawings

Figure 1 is a flow chart of a bright spot positioning method provided by an embodiment of the present invention;

Figure 2 is a flow chart of step S102 in Figure 1;

Figure 3 is a flow chart of step S104 in Figure 1;

Figure 4 is a flow chart of step S105 in Figure 1;

Figure 5 is a flow chart of step S106 in Figure 1;

Figure 6 is a flow chart of step S107 in Figure 1;

Figure 7 is a flow chart of step S108 in Figure 1;

Figure 8 is a schematic structural diagram of a bright spot positioning device provided by an embodiment of the present invention;

Figure 9 is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

It should be noted that although the functional modules are divided in the device schematic diagram and the logical sequence is shown in the flow chart, in some cases, the modules can be divided into different modules in the device or the order in the flow chart can be executed. The steps shown or described. The terms "first", "second", etc. in the description, claims, and above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific sequence or sequence.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which the invention belongs. The terminology used herein is for the purpose of describing embodiments of the present invention only and is not intended to limit the present invention.

First, some terms involved in this invention are analyzed:

Artificial intelligence (AI): It is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence; artificial intelligence is a branch of computer science, artificial intelligence Intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a manner similar to human intelligence. Research in this field includes robotics, language recognition, image recognition, natural language processing, and expert systems. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

The binarization of the image is to set the grayscale value of the pixels on the image to 0 or 255, which means that the entire image presents an obvious visual effect of only black and white.

Center of gravity method: In physics, if you want an object to remain vertically stationary or move in a straight line at a uniform speed, then this point is the center of gravity. The center of gravity method first marks the position of each location in the coordinate system, with the purpose of determining the relative distance of each point. In international site selection, longitude and latitude are often used to establish coordinates. Then, based on the horizontal and vertical coordinate values of each point in the coordinate system, the location coordinates X and Y with the lowest transportation cost are obtained.

Interpolation: Interpolate a continuous function on the basis of discrete data, so that this continuous curve passes through all discrete points, and the approximate value of the function at other points can also be estimated.

Spline interpolation: A mathematical method that uses variable splines to create a smooth curve through a series of points. The interpolation spline is composed of some polynomials. Each polynomial is determined by two adjacent data points. Any two adjacent polynomials and their derivatives are continuous at the connecting points.

Spline interpolation method: A function is determined between every two points. This function is a spline. Different functions lead to different splines. Then all splines are segmented into one function, which is the final interpolation function.

Current bright spot positioning methods often have problems such as inaccurate background estimation and uneven bright spot segmentation during image processing, which affects the accuracy of bright spot positioning. Therefore, how to improve the accuracy of bright spot positioning has become an urgent technical problem to be solved.

Based on this, embodiments of the present invention provide a bright spot positioning method, a bright spot positioning device, electronic equipment and a storage medium, aiming to improve the accuracy of bright spot positioning.

The bright spot positioning method, bright spot positioning device, electronic equipment and storage medium provided by the embodiments of the present invention are specifically described through the following embodiments. First, the bright spot positioning method in the embodiment of the present invention is described.

Embodiments of the present invention can acquire and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .

Basic artificial intelligence technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes computer vision technology, robotics technology, biometric technology, speech processing technology, natural language processing technology, and machine learning/deep learning.

The bright spot positioning method provided by the embodiment of the present invention relates to the field of artificial intelligence technology. The bright spot positioning method provided by the embodiment of the present invention can be applied in a terminal or a server, or can be software running in the terminal or the server. In some embodiments, the terminal can be a smartphone, a tablet, a laptop, a desktop computer, etc.; the server can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers. It can be configured to provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Server; software can be an application that implements a bright spot positioning method, etc., but is not limited to the above forms.

The present invention may be used in a variety of general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, including Distributed computing environment for any of the above systems or devices, etc. The present invention can be Described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

Figure 1 is an optional flow chart of a bright spot positioning method provided by an embodiment of the present invention. The method in Figure 1 may include, but is not limited to, steps S101 to S108.

Step S101, obtain the original grayscale image to be processed;

Step S102, perform image preprocessing on the original grayscale image to obtain an initial background image;

Step S103, perform ratio calculation on the original grayscale image and the initial background image to obtain a signal-to-noise ratio matrix;

Step S104, perform binary segmentation on the signal-to-noise ratio matrix to obtain a binary image;

Step S108, perform image equalization processing on the original grayscale image based on the binary image to obtain an initial grayscale image;

Step S106, perform image filtering processing on the initial grayscale image to obtain the target grayscale image;

Step S107: Perform bright spot detection on the target grayscale image based on the binary image to obtain the target bright spot;

Step S108: perform positioning processing on the target bright spot through a preset algorithm to obtain position data and light intensity data of the target bright spot.

The steps S101 to S108 shown in the embodiment of the present invention are to obtain the original grayscale image to be processed; perform image preprocessing on the original grayscale image to obtain an initial background image; and perform a ratio calculation on the original grayscale image and the initial background image. , obtain the signal-to-noise ratio matrix, and perform binary segmentation on the signal-to-noise ratio matrix to obtain a binary image, which can improve the accuracy of image background estimation and image segmentation. Further, image equalization processing is performed on the original grayscale image according to the binary image to obtain an initial grayscale image, which can globally equalize the initial grayscale image. At the same time, by performing image filtering processing on the initial grayscale image, the target grayscale image is obtained. degree image, which can improve the quality of the target image. Finally, perform highlight detection on the target grayscale image based on the binary image to obtain the target highlight; perform positioning processing on the target highlight through a preset algorithm to obtain the position data and light intensity data of the target highlight. This method can more accurately identify the target highlight. Identify the target bright spots and determine the location of the target bright spots, thereby improving the accuracy of bright spot positioning.

In step S101 of some embodiments, a fluorescence grayscale image can be acquired by camera shooting, and the captured fluorescence grayscale image can be used as the original grayscale image to be processed, wherein the image format of the original grayscale image can be TIF format, Image size can be 4116×2176.

Please refer to Figure 2. In some embodiments, in order to improve the accuracy of background estimation and improve the image quality of the acquired background image and foreground image, step S102 may include but is not limited to steps S201 to step S203:

Step S201, perform histogram statistics on the original grayscale image within a preset interval to obtain a brightness histogram;

Step S202, perform feature extraction on the brightness histogram to obtain local background values;

Step S203: perform background subtraction processing on the original grayscale image according to the local background value to obtain an initial background image and an initial background-removed image.

In step S201 of some embodiments, the size of the preset interval can be set according to actual business requirements. For example, the size of the preset interval can be n*n, where n is an integer greater than 0, for example, it can be the original grayscale. Within the n×n range of image f, perform histogram statistics on the brightness of the original grayscale image to obtain a brightness histogram, where the brightness histogram can be expressed as formula (1):
Hist(i,j)＝∑f(i,j)＝I|(i,j)∈D _n*n formula (1)

Among them, Hist(i, j) is the brightness histogram of the preset interval [n*n], f is the original grayscale image, i, j is the pixel point coordinates of the original grayscale image, and D _n*n is the preset interval , I is the light intensity value of the original grayscale image.

In step S202 of some embodiments, feature extraction is performed on the brightness histogram, and the 30th percentile of the brightness histogram is taken as the local background value. It should be noted that in some other embodiments, other quantiles on the brightness histogram can also be selected as the local background value, but it is not limited thereto.

In step S203 of some embodiments, when performing background subtraction processing on the original grayscale image according to the local background value, the local background value is subtracted from the original grayscale image to obtain the initial background image and the initial de-background image. The specific process can be expressed as As shown in formula (2):
f ₁ ={f-Hist ₃₀ |D}Formula (2)

Among them, f ₁ is the initial background removal image, and Hist ₃₀ is the local background value, that is, the initial background image.

The above steps S201 to S203 use local histogram statistics to perform background estimation, which can flexibly cope with high-density and low-density fluorescent chips, and can be better compatible with background estimation under different chip densities, thereby improving the accuracy of background estimation. accuracy.

In step S102 of some embodiments, the ratio calculation of the original grayscale image and the initial background image can be performed to easily obtain the signal-to-noise ratio of the original grayscale image, that is, the signal-to-noise ratio matrix, where the signal-to-noise ratio matrix can be expressed as follows Formula (3) shows:
SNR=f/g={f(x,y)/g(x,y)|(x,y)∈D}Formula (3)

Among them, SNR is the signal-to-noise ratio matrix, f(x, y) is the original grayscale image, g(x, y) is the initial background image, where x, y are the coordinates of the pixel point, and D is the preset interval.

Referring to Figure 3, in some embodiments, step S103 may include, but is not limited to, steps S301 to S302:

Step S301, perform threshold calculation based on the signal-to-noise ratio matrix and the brightness histogram to obtain a binarized threshold;

Step S302: Binarize and segment the signal-to-noise ratio matrix according to the binarization threshold to obtain a binarized image.

Since laser illumination is often uneven during image segmentation, this will cause the brightness value to pass through The computational complexity of preliminary screening of bright spots is high. Therefore, in order to solve this problem, the present invention adopts a signal-to-noise ratio screening method that is less affected by illumination to realize the preliminary screening of bright spots. The preliminary screening process is embodied in the present invention. Image segmentation process.

In step S301 of some embodiments, in order to eliminate the problem of unstable threshold selection in traditional technology, embodiments of the present invention use the characteristics of the inverse proportional function to achieve adaptation to different densities. Specifically, first sort the pixels in the signal-to-noise ratio matrix according to the pixel size of the pixels in the signal-to-noise ratio matrix. In this way, a set of arrays arranged from small to large can be obtained. For example, the array is 8956416 wait. Further, the median and higher quantile of the signal-to-noise ratio in the signal-to-noise ratio matrix are calculated based on the array, and the binary value is calculated based on the array and the calculated median A and higher quantile B. Binarization threshold, where the binarization threshold can be expressed as shown in formula (4):
T=mean(SNR)*(factor+A/(B/A)) formula (4)

Among them, T is the binarization threshold, mean (SNR) is the mean of the signal-to-noise ratio matrix, A is the median of the signal-to-noise ratio matrix, A can be Quantile _SNR (50), that is, the median is the signal-to-noise ratio matrix _{The 50th} quantile of The quantile of , factor is the preset weight parameter, which is generally 0.5.

Further, the above binarization threshold can be expressed as
T＝mean(SNR)*(factor+Quantile _SNR (50)/(Quantile _SNR (95)/(Quantile _SNR (50)))

In step S302 of some embodiments, the signal-to-noise ratio matrix is binarized and segmented according to the binarization threshold, thereby obtaining the binarized image BI.

The above-mentioned steps S301 to S302 perform binary segmentation of the image by using the inverse proportional algorithm of histogram statistical quantiles, which can effectively deal with the segmentation of bright spots under different densities, and can easily change the size of the binarization threshold according to the density. , when the density is high, a smaller binarization threshold is selected, and when the density is low, a larger binarization threshold is selected, thereby solving the problem of overestimation of the background in high-density scenes, thus improving the background estimation and Image segmentation accuracy.

Referring to Figure 4, in some embodiments, step S104 may include, but is not limited to, steps S401 to S402:

Step S401: Calculate the brightness of the original grayscale image based on the binarized image to obtain the mean foreground brightness of the binarized image;

Step S402: Perform brightness normalization on the original grayscale image according to the average foreground brightness to obtain an initial grayscale image.

Since during the shooting process, there are often certain differences in the illumination of different areas of the image, that is, the image will appear bright in the center and dark at the edges. Therefore, in order to balance the illumination differences in different areas of the image, it is often necessary to perform global brightness equalization of the image. , thereby improving image quality.

In step S401 of some embodiments, the fluorescence intensity of different areas of the original grayscale image is first evaluated. Specifically, the average fluorescence intensity within a fixed range around each pixel point can be counted, and the average fluorescence brightness value is used as the average value of the fluorescence intensity of the pixel point. Fluorescence intensity, where the size of the fixed range can be determined according to actual business Requirement settings, no restrictions. For example, find the pixels with a pixel value of 1 in the 161×161 area of the binary image, then obtain the mean value of the foreground elements of these pixels in the foreground image, and use this mean value of the foreground elements as the mean foreground brightness of the binary image .

Furthermore, the process of using morphology to calculate the mean value of the foreground elements in the D region can be expressed as shown in formula (5):

Among them, f’ is the fluorescence evaluation matrix (i.e., the mean value of foreground brightness), m and n represent the height and width of the original grayscale image respectively, and f is the original grayscale image after background removal (i.e., the initial background-removed image). Mean refers to finding the mean value of the foreground elements of the foreground image with a pixel value of 1 in the binary image B in the D area for the structural element D. B is a binary image in which the original grayscale image is segmented according to the binary threshold T. , B=0 represents the background, and B=1 represents the foreground.

In step S402 of some embodiments, the fluorescence evaluation matrix can be used to characterize the brightness differences in different regional positions of the original grayscale image. Therefore, the brightness differences in different regional positions can be eliminated according to the fluorescence evaluation matrix, that is, the brightness differences of the original grayscale image. The brightness is global brightness normalized based on the image center to obtain the initial grayscale image.

In a specific embodiment, taking the equalized pixel (x, y) as an example, first calculate the ratio ratio between the center of the evaluation matrix and the pixel point. This ratio can be used to reflect the factor by which the intensity at that location needs to be increased. , where ratio=center(f')/f'(x,y). Furthermore, the ratio is multiplied by the pixel of the original grayscale image to obtain the normalized brightness value of the pixel. Finally, each normalized pixel point of the original grayscale image is updated to the brightness center to obtain the initial grayscale image, where the initial grayscale image can be expressed as

g'(x,y)=g(x,y)*center(f')/f'(x,y); where, g'(x,y) is the initial grayscale image, g(x,y) is the original grayscale image.

In the above steps S401 to S402, global equalization can be used to effectively eliminate the impact of uneven lighting on image quality when capturing images, improve the detection rate of edge bright spots, reduce the probability of missed detection, and increase the total amount of data.

Referring to Figure 5, in some embodiments, step S105 may include, but is not limited to, steps S501 to S502:

Step S501, perform Gaussian filtering on the initial grayscale image to obtain the first filtered image;

Step S502: Perform Laplacian sharpening processing on the first filtered image to obtain a target grayscale image.

In order to prevent the Laplacian filter from causing more image noise when calculating the second differential, the present invention uses a two-step method to perform LoG filtering, that is, first perform Gaussian blur filtering on the initial grayscale image, and then perform Gaussian blur filtering on the image after Gaussian filtering. Laplacian sharpening filter.

In step S501 of some embodiments, Gaussian filtering is performed on the initial grayscale image through a Gaussian filter to remove image noise, where the image noise includes Gaussian noise and salt-and-pepper noise, and we obtain First filtered image. Specifically, the initial grayscale image can be subjected to Gaussian convolution processing according to a window size of 3×3 through a Gaussian filter to obtain the first filtered image, where the Gaussian kernel can be 0.85.

In step S502 of some embodiments, since current image imaging often has problems such as dispersion and astigmatism, the center of the image will be clear and the edges will be blurry. In order to solve this problem, rh Mexico in the embodiment of the present invention The hat operator adopts a gradient method. The kernel parameters for sharpening are different for different areas of the first filtered image. The kernel parameters are generally determined based on the blur condition of the image in this area. Specifically, through rh Mexico with multiple kernel parameters The hat operator performs sharpening convolution on the first filtered image to obtain the target grayscale image. The sharpening process can be expressed as rh(x, y)=rh*factor, where factor is the sharpening factor. If a certain pixel position of the first filtered image is locally blurred, the sharpening factor factor will be larger, so that The pixel position gets a higher degree of sharpening.

In the above steps S501 to S502, Gaussian noise and salt-and-pepper noise can be easily removed through Gaussian filtering. Laplacian sharpening can better enhance the highlight features and improve the contrast between the foreground and background of the target grayscale image. , At the same time, the LoG filter operator parameters adopt a regional gradient filtering strategy, which can improve the resolution of the target bright spots and also improve the discrimination of the target bright spots, where the target bright spots can be target fluorescence peak points.

Referring to Figure 6, in some embodiments, step S106 includes but is not limited to steps S601 to S605:

Step S601: Perform highlight detection on the target grayscale image based on the binarized image to obtain the average foreground brightness of the binarized image;

Step S602, determine the intensity threshold for bright spot detection based on the average foreground brightness;

Step S603: Construct a bright spot connected graph based on the adjacency relationship between candidate bright spots in the target grayscale image;

Step S604: Screen the candidate bright spots of the bright spot connected graph according to the brightness values of the candidate bright spots to obtain initial bright spots;

Step S605: Detect the initial bright spots according to the preset filtering conditions to obtain the target bright spots.

In step S601 of some embodiments, the mean function is used to calculate the brightness of foreground pixels with a pixel of 1 in the binary image to obtain the mean foreground brightness of the binary image.

In step S602 of some embodiments, the process of determining the intensity threshold for bright spot detection according to the mean foreground brightness can be expressed as Threshold=mean(f _BI=1 ), where Threshold is the intensity threshold and f _BI=1 is binary. Value the foreground pixels with a value of 1 in the image, and mean(f _BI=1 ) is to find the mean.

In step S603 of some embodiments, a 4-connected or 8-connected method is often used to construct a bright spot connected graph based on the adjacency relationship between candidate bright spots. In the present invention, an 8-connected method is used to construct a bright spot connected graph for the adjacency relationships between candidate bright spots.

In step S604 of some embodiments, according to the bright spot connected graph, find 8 candidate bright spots adjacent to each candidate bright spot, and compare the 8 candidate bright spots adjacent to each candidate bright spot. If the brightness value of the candidate bright spot at the center is greater than the adjacent the brightness values of the 8 candidate bright spots, then the candidate bright spots are used as the initial bright spots. point.

In step S605 of some embodiments, the preset filtering conditions can be set based on multi-dimensional features such as intensity thresholds, binarized area parameters, Gaussian coefficients, and gradient direction fields. Specifically, the initial bright spot can be detected and filtered based on two inequalities (as shown in formula (6) and formula (7)). When the initial bright spot satisfies the following two inequality conditions at the same time, the initial bright spot is used as the target bright spot. .
Area>0.5 formula (6)
SumIntensity _5*5 *GaussCeof*GradientCeof>Threshold formula (7)

Among them, Area is the proportion of the foreground with a binary image of 1 in the bright spot connected map, that is, more than half of the pixels in the 3*3 range of the brightness connected map should be pixels representing the foreground. SumIntensity is the integral sum of energy within a range of 5*5 near the initial bright spot. SumIntensity can reflect the amount of energy that the initial bright spot can excite. GaussCeof is the correlation coefficient of window pixels with a window size of 5*5 and a two-dimensional Gaussian distribution. The correlation coefficient can be obtained by performing Gaussian fitting on a large number of known bright points. The correlation coefficient can reflect the bright point shape of the initial bright points. If The closer the correlation coefficient is to 1, the closer the shape of the current area is to the shape of the known bright spot. GradienCeof represents the gradient direction field within a window with a window size of 5*5. If the horizontal gradient direction and vertical gradient direction of a pixel (initial bright spot) point to the central peak, the larger the value of the gradient direction field, the more this parameter can reflect Peak trends in images.

In the above step S601 to step S605, during the process of locating target bright spots, multi-dimensional features such as intensity threshold, binarized area parameter, Gaussian coefficient and gradient direction field are integrated to screen candidate bright spots, which can more accurately find out All bright peak points in the original grayscale image can effectively improve the recall rate and precision rate of target bright spot search.

Please refer to Figure 7. In some embodiments, the position data includes sub-pixel center coordinates, and the light intensity data includes light intensity values. Step S107 may include but is not limited to steps S701 to S702:

Step S701, identify the coordinates of the target bright spot through the center of gravity method, and obtain the sub-pixel center coordinates of the target bright spot;

Step S702: Extract the brightness of the sub-pixel center coordinates through the quadratic spline interpolation method to obtain the light intensity value of the target bright spot.

In steps S701 and S702 in some embodiments, the relative distance between the target bright spots is calculated through the center of gravity method, and the sub-pixel center coordinates of the target bright spots are obtained according to the size of the relative distance, where the target bright spots are peak points. The brightness of the sub-pixel center coordinates is extracted through quadratic spline interpolation or a quadratic function to obtain the light intensity value of the target bright spot.

It should be noted that in the above-mentioned image preprocessing process and target highlight screening process, the x86 instruction set can be used for accelerated processing, so that the highlight positioning method according to the embodiment of the present invention can meet the processing needs of real-time sequencing and industrial production. requirements.

The brightness positioning method of the embodiment of the present invention obtains the original grayscale image to be processed; performs image preprocessing on the original grayscale image to obtain an initial background image; and performs image processing on the original grayscale image and the initial background. The image is ratio calculated to obtain a signal-to-noise ratio matrix, and the signal-to-noise ratio matrix is binarized and segmented to obtain a binarized image, which can improve the accuracy of image background estimation and image segmentation. Further, image equalization processing is performed on the original grayscale image according to the binary image to obtain an initial grayscale image, which can globally equalize the initial grayscale image. At the same time, by performing image filtering processing on the initial grayscale image, the target grayscale image is obtained. degree image, which can improve the quality of the target image. Finally, perform highlight detection on the target grayscale image based on the binary image to obtain the target highlight; perform positioning processing on the target highlight through a preset algorithm to obtain the position data and light intensity data of the target highlight. This method can more accurately identify the target highlight. Identify the target bright spots and determine the location of the target bright spots, thereby improving the accuracy of bright spot positioning.

Please refer to Figure 8. An embodiment of the present invention also provides a bright spot positioning device that can implement the above bright spot positioning method. The device includes:

Image acquisition module 801, used to acquire the original grayscale image to be processed;

Image preprocessing module 802 is used to perform image preprocessing on the original grayscale image to obtain an initial background image;

The calculation module 803 is used to calculate the ratio of the original grayscale image and the initial background image to obtain the signal-to-noise ratio matrix;

Image segmentation module 804 is used to perform binary segmentation on the signal-to-noise ratio matrix to obtain a binary image;

The image equalization module 805 is used to perform image equalization processing on the original grayscale image based on the binary image to obtain an initial grayscale image;

The image filtering module 806 is used to perform image filtering processing on the initial grayscale image to obtain the target grayscale image;

The bright spot detection module 807 is used to detect bright spots on the target grayscale image based on the binary image to obtain the target bright spots;

The positioning module 808 is used to position the target bright spot through a preset algorithm to obtain the position data and light intensity data of the target bright spot.

The specific implementation of the bright point positioning device is basically the same as the specific embodiment of the above-mentioned bright point positioning method, and will not be described again here.

An embodiment of the present invention also provides an electronic device. The electronic device includes: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. , the above bright spot positioning method is implemented when the program is executed by the processor. The electronic device can be any smart terminal including a tablet computer, a vehicle-mounted computer, etc.

Please refer to Figure 9, which illustrates the hardware structure of an electronic device according to another embodiment. The electronic device includes:

The processor 901 can be implemented by a general CPU (Central Processing Unit, central processing unit), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement The technical solutions provided by the embodiments of the present invention;

The memory 902 can be a read-only memory (ReadOnlyMemory, ROM) or a static storage device. It can be implemented in the form of equipment, dynamic storage device or random access memory (RandomAccessMemory, RAM). The memory 902 can store operating systems and other application programs. When the technical solutions provided by the embodiments of this specification are implemented through software or firmware, the relevant program codes are stored in the memory 902 and called by the processor 901 to execute the implementation of the present invention. Example of bright spot positioning method;

Input/output interface 903, used to implement information input and output;

Communication interface 904 is used to realize communication interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wirelessly (such as mobile network, WIFI, Bluetooth, etc.);

Bus 905, which transmits information between various components of the device (such as processor 901, memory 902, input/output interface 903, and communication interface 904);

The processor 901, the memory 902, the input/output interface 903 and the communication interface 904 implement communication connections between each other within the device through the bus 905.

Embodiments of the present invention also provide a storage medium. The storage medium is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be processed by one or more The processor is executed to implement the above bright spot positioning method.

As a non-transitory computer-readable storage medium, memory can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, and the remote memory may be connected to the processor via a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.

The bright spot positioning method, bright spot positioning device, electronic equipment and storage medium provided by embodiments of the present invention obtain an original grayscale image to be processed; perform image preprocessing on the original grayscale image to obtain an initial background image; and perform image preprocessing on the original grayscale image. The ratio between the image and the initial background image is calculated to obtain the signal-to-noise ratio matrix, and the signal-to-noise ratio matrix is binarized and segmented to obtain a binarized image, which can improve the accuracy of image background estimation and image segmentation. Further, image equalization processing is performed on the original grayscale image according to the binary image to obtain an initial grayscale image, which can globally equalize the initial grayscale image. At the same time, by performing image filtering processing on the initial grayscale image, the target grayscale image is obtained. degree image, which can improve the quality of the target image. Finally, perform highlight detection on the target grayscale image based on the binary image to obtain the target highlight; perform positioning processing on the target highlight through a preset algorithm to obtain the position data and light intensity data of the target highlight. This method can more accurately identify the target highlight. Identify the target bright spots and determine the location of the target bright spots, thereby improving the accuracy of bright spot positioning.

The embodiments described in the embodiments of the present invention are to more clearly illustrate the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. Those skilled in the art will know that with the evolution of technology and new technologies, As application scenarios arise, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

Those skilled in the art can understand that the technical solutions shown in Figures 1-7 do not limit the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different A step of.

The device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separate, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Those of ordinary skill in the art can understand that all or some steps, systems, and functional modules/units in the devices disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof.

The terms "first", "second", "third", "fourth", etc. (if present) in the description of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe specific objects. Sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the invention described herein are capable of being practiced in sequences other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus that encompasses a series of steps or units and need not be limited to those explicitly listed. Those steps or elements may instead include other steps or elements not expressly listed or inherent to the process, method, product or apparatus.

It should be understood that in the present invention, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And/or" is used to describe the relationship between associated objects, indicating that there can be three relationships. For example, "A and/or B" can mean: only A exists, only B exists, and A and B exist simultaneously. , where A and B can be singular or plural. The character "/" generally indicates that the related objects are in an "or" relationship. “At least one of the following” or similar expressions thereof refers to any combination of these items, including any combination of a single item (items) or a plurality of items (items). For example, at least one item (item) of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c" ”, where a, b, c can be single or multiple.

In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the above units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or may be Integrated into another system, or some features can be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be in electrical, mechanical or other forms.

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

In addition, each functional unit in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or software functional units.

Integrated units may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as independent products. Based on this understanding, the technical solution of the present invention is essentially or contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. that can store programs. medium.

The preferred embodiments of the embodiments of the present invention have been described above with reference to the accompanying drawings, but the scope of rights of the embodiments of the present invention is not thereby limited. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present invention shall be within the scope of rights of the embodiments of the present invention.

Claims (10)

1. A bright spot positioning method, characterized in that the method includes:

   Get the original grayscale image to be processed;

   Perform image preprocessing on the original grayscale image to obtain an initial background image;

   Perform ratio calculation on the original grayscale image and the initial background image to obtain a signal-to-noise ratio matrix;

   Perform binary segmentation on the signal-to-noise ratio matrix to obtain a binary image;

   Perform image equalization processing on the original grayscale image according to the binary image to obtain an initial grayscale image;

   Perform image filtering processing on the initial grayscale image to obtain a target grayscale image;

   Perform bright spot detection on the target grayscale image according to the binary image to obtain the target bright spot;

   The target bright spot is positioned through a preset algorithm to obtain the position data and light intensity data of the target bright spot.
2. The bright spot positioning method according to claim 1, characterized in that the step of performing image preprocessing on the original grayscale image to obtain an initial background image includes:

   Perform histogram statistics on the original grayscale image within a preset interval to obtain a brightness histogram;

   Perform feature extraction on the brightness histogram to obtain local background values;

   Perform background subtraction processing on the original grayscale image according to the local background value to obtain the initial background image and an initial background-removed image.
3. The method of locating a bright spot according to claim 2, wherein the step of binary segmenting the signal-to-noise ratio matrix to obtain a binary image includes:

   Perform threshold calculation according to the signal-to-noise ratio matrix and the brightness histogram to obtain a binarized threshold;

   Binarize and segment the signal-to-noise ratio matrix according to the binarization threshold to obtain the binarized image.
4. The method of locating bright spots according to claim 1, wherein the step of performing image equalization processing on the original grayscale image according to the binary image to obtain an initial grayscale image includes:

   Perform brightness calculation on the original grayscale image according to the binarized image to obtain the mean foreground brightness of the binarized image;

   The original grayscale image is brightness normalized according to the foreground brightness mean value to obtain the initial grayscale image.
5. The method of locating bright spots according to claim 1, wherein the step of performing image filtering on the initial grayscale image to obtain a target grayscale image includes:

   Perform Gaussian filtering on the initial grayscale image to obtain a first filtered image;

   Perform Laplacian sharpening processing on the first filtered image to obtain the target grayscale image.
6. The bright spot positioning method according to claim 1, characterized in that the step of performing bright spot detection on the target grayscale image according to the binary image to obtain the target bright spot includes:

   Perform highlight detection on the target grayscale image according to the binarized image to obtain the mean foreground brightness of the binarized image;

   Determine an intensity threshold for bright spot detection based on the average foreground brightness;

   Construct a bright spot connected graph according to the adjacency relationship between candidate bright spots in the target grayscale image;

   Filter the candidate bright spots of the bright spot connected graph according to the brightness values of the candidate bright spots to obtain initial bright spots;

   The initial bright spots are detected according to preset filtering conditions to obtain the target bright spots.
7. The bright spot positioning method according to any one of claims 1 to 6, wherein the position data includes sub-pixel center coordinates, the light intensity data includes a light intensity value, and the target is determined by a preset algorithm. The steps of positioning the bright spot and obtaining the position data and light intensity data of the target bright spot include:

   Perform coordinate identification on the target bright spot through the center of gravity method to obtain the sub-pixel center coordinates of the target bright spot;

   The brightness of the sub-pixel center coordinates is extracted using a quadratic spline interpolation method to obtain the light intensity value of the target bright spot.
8. A bright spot positioning device, characterized in that the device includes:

   Image acquisition module, used to acquire the original grayscale image to be processed;

   An image preprocessing module, used to perform image preprocessing on the original grayscale image to obtain an initial background image;

   A calculation module for performing ratio calculation on the original grayscale image and the initial background image to obtain a signal-to-noise ratio matrix;

   An image segmentation module, used to perform binary segmentation on the signal-to-noise ratio matrix to obtain a binary image;

   An image equalization module, configured to perform image equalization processing on the original grayscale image according to the binary image to obtain an initial grayscale image;

   An image filtering module, used to perform image filtering processing on the initial grayscale image to obtain a target grayscale image;

   A bright spot detection module, configured to perform bright spot detection on the target grayscale image based on the binary image to obtain the target bright spot;

   A positioning module is used to perform positioning processing on the target bright spot through a preset algorithm to obtain position data and light intensity data of the target bright spot.
9. An electronic device, characterized in that the electronic device includes a memory, a processor, a program stored on the memory and executable on the processor, and a program for realizing communication between the processor and the memory. A data bus is connected to the communication, and when the program is executed by the processor, the steps of the bright spot positioning method according to any one of claims 1 to 7 are implemented.
10. A storage medium, the storage medium is a computer-readable storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs can be used by one or more A plurality of processors are executed to implement the steps of the bright spot positioning method described in any one of claims 1 to 7.