# Optimization of the Index System of the Sports Social Organization Development Ability Relying on Image Optimization, Recognition, and Simulation

Authors: Wei Sun
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Sports social organizations refer to nonprofit, nongovernmental, and group organizations that achieve specific service goals and common aspirations and spontaneously form various sports and fitness activities in accordance with the rules. This article aims to study how to optimize the index system of the capacity developments by relying on image optimization and recognition simulation. This article puts forward the relevant theoretical knowledge of image optimization and recognition and capacity developments and proposes two algorithms based on image optimization and recognition. In 2019, the percentage of growth of sports social organizations rose from 6.7% to the final 27%; in 2020, the growth rate of sports social organizations is getting higher and higher, from 9% at the beginning to 34% at the end. It can be seen that people’s love for sports activities is getting higher and higher, and the state is paying more and more attention to sports social organizations. The factors that affect the development ability of sports social organizations include the quality, education, and management capabilities of the members of the organization, and the country and the government should also take corresponding measures. The improvement of the development ability of sports social organizations is of great significance to the development of sports social organizations.


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## Body

## 1. Introduction

At this stage, China has entered a social transformation period of innovation and comprehensive reform. As one of the main lines of the reform and development of the social field, social organization has a certain social necessity to deeply explore its modern organizational development model with Chinese characteristics. As the intermediary and hub connecting the sports power of the nation and the nationwide fitness program, the importance of its existence and development is self-evident. The development of sports social organizations is inseparable from the social environment, in which they are located, and the guidance of national policies and regulations plays a vital role in the growth and development of the organization.The capacity developments refer to the correct control of the actual ability of sports social organizations, and related sports activities are shown by the interaction of political, economic, social, and regional environments related to themselves. It has the conditions for internal management functions, service functions, and social influence. Image recognition technology is an important technology in the information age, and its purpose is to allow computers to replace humans to process a large amount of physical information. With the development of computer technology, human beings have a deeper and deeper understanding of image recognition technology. The process of image recognition technology is divided into information acquisition, preprocessing, feature extraction and selection, classifier design, and classification decision-making.With the development of society, the field of sports has attracted more and more attention. Chouksey and Jha found that image segmentation can generally be used for object recognition and detection. In the field of image segmentation, the multilevel threshold method is one of the leading methods. However, the computational cost of this method is high. They proposed a new algorithm, which is the quasiinverse multiverse optimization algorithm. It can perform image segmentation by finding the optimal threshold. The disadvantage of this experiment is that the advantages and disadvantages of the two methods are not compared, and the new algorithm is not prominent [1]. Quan et al. found that face recognition is very important in computer vision. In order to solve some of the problems in traditional face recognition, they proposed a new optimization method for face recognition. This method allows people to match the best state of the energy function more quickly and accurately. But the disadvantage is that they did not propose how to use this method specifically, and the reliability of the method has not been proven [2]. Gupta et al. found that it is a difficult task to make image recognition the lowest cost and highest efficiency. In order to reduce the cost of identification and improve efficiency, they proposed a new multiobjective optimization framework. In this work, they proposed a better harmony search algorithm than traditional algorithms. But they did not mention the advantages of the algorithm in comparison, so the authenticity is still uncertain [3]. Schubert et al. found that embedded image retrieval problems in robotic tasks provide additional structures that can be utilized, so they proposed a graph-based framework to systematically utilize different types of additional structures and information. They believed that this method can also quickly collect other knowledge about database image poses, but this method has not designed an experiment to prove it, so there are still many doubtful places [4]. Song et al. found that deep learning is widely used in the field of machine vision. To obtain effective image recognition, a large amount of data must be collected; otherwise, it is easy to make mistakes. The neural network in deep learning can rely on the improved network to generate a highly diversified balanced image dataset. Simulation results show that the algorithm’s image recognition speed is faster and more efficient. However, no specific examples are used to complete the analysis, so this is a shortcoming of the experiment [5]. Gurevich and Yashina discussed the modern development of descriptive image analysis and its development potential. Descriptive image analysis is one of the leading and intensive development areas of modern mathematical image analysis theory. Their research is dedicated to standardizing descriptive algorithm image analysis and the generation of recognition schemes. However, their research has not been supported by related experiments. A variety of experimental comparisons should be carried out to get the best method [6]. Long and Ning found that in computer vision, it becomes difficult to identify objects from rotated and zoomed images. The log-polar coordinate transformation is a mapping method that is invariant to rotation and scale. However, this method has many problems, so they proposed a new template matching algorithm based on global LPT. They were also inspired by pigeons to optimize the search strategy to complete the object recognition of drones with varying target rotations and scales. However, this method has not been proved to be effective through experiments, and the two methods have not been compared [7]. Revina and Sam Emmanuel are very interested in facial expression recognition. They think it is very interesting because it enables people to recognize facial expressions in daily life. However, most traditional methods cannot accurately recognize facial expressions because facial expressions are based on the movement of the human face. The facial recognition method they proposed improves the accuracy of face recognition. The experiment of the algorithm they proposed is carried out using a database, but there are many face recognition methods, and there is no specific description of which method is not good [8]. Through the experimental analysis of the scholars, we can know the following: they agree that relying on image optimization and recognition for simulation in various fields has been widely used. For image recognition and various facial recognition, it is of great significance to society. However, the experiment did not mention the comparison of the algorithms before and after the improvement, so it is impossible to determine which method is the best.The innovations of this paper are as follows. (1) It introduces the relevant theoretical knowledge of image optimization recognition simulation and the capacity development and using the target detection and recognition technology algorithm to analyze how to study the optimization of the index system of the capacity developments. (2) It conducts experiments and analysis on the capacity developments based on target detection and recognition technology algorithms. Through investigation and analysis, it is found that if the capacity developments are improved, it will be beneficial to the development of society.


## 2. Target Detection and Recognition Technology Algorithm Based on Image Optimization and Recognition

With the widespread application of artificial intelligence technology, especially deep learning algorithms in the field of computer vision, the accuracy and real-time performance of target detection and recognition technology in images and videos are getting higher and higher. More and more relevant research results have been published in various top journals and conferences on artificial intelligence, computer vision, and pattern recognition, and more and more computer vision start-up companies are applying this technology to real scenes. On various public datasets, the level of target detection and recognition algorithms continues to rise, and the performance of algorithms is constantly approaching or even surpassing humans.Optical information processing refers to the generation, transmission, detection, and processing of optical images. Necessary images are called signals, and unnecessary noise may be generated during processing. The input information of optical information processing is optical information converted from optical information such as images, electrical information, or sound information. Compared with digital image processing, optical processing system has strong information processing ability and fast calculation speed, especially parallel processing, which is especially suitable for high-speed, real-time processing of images [9]. As the main branch of optical information processing, the optical image recognition technology developed based on Fourier optics fully inherits the previously mentioned characteristics. It performs parallel processing on all sampling points and performs Fourier transform at the speed of light [10]. The simulation process is shown in Figure 1:Figure 1
Image simulation process.As shown in Figure1, optimizing the recognition of complex targets in the image can effectively improve the speed and accuracy of target recognition in the human-machine interface. Image target recognition needs to extract the color and texture characteristics of image information, which is completed through decision-making fusion processing [11].


### 2.1. Target Monitoring and Recognition Technology Algorithm

The interframe difference method is a method of obtaining the contour of a moving target by performing a difference operation on two adjacent frames in a video image sequence. It can be well adapted to the situation where there are multiple moving targets and camera movement. The interframe difference method is a method that uses the relationship between the prestation and poststation in the image sequence to detect the moving area. It is shown in Figure2.Figure 2
Motion between frame differences.As shown in Figure2, when there is abnormal object movement in the surveillance scene, there will be more obvious differences between frames. The two frames are subtracted to obtain the absolute value of the brightness difference of the two frames. It judges whether it is greater than the threshold to analyze the motion characteristics of the video or image sequence and determines whether there is any object movement in the image sequence. The frame-by-frame difference of the image sequence is equivalent to high-pass filtering of the image sequence in the time domain. Discrimination of motion area or background is as follows:(1)Mask1a,b=1,Ita,b≻Td,0,otherwise.In the formula,Td is the discrimination threshold.


#### 2.1.1. HOG Features

The calculation formula of gradient strength and direction is as follows:(2)h=Ix2+IY2.Finally, it creates a direction histogram with each cell in the image as a unit.


#### 2.1.2. Decision Area and Decision Function

In the field of machine learning, the goal of classification is to gather objects with similar characteristics. A linear classifier makes classification decisions through linear combinations of features to achieve this goal. The characteristics of objects are usually described as eigenvalues, and in vectors, they are described as eigenvectors. The classifier has been applied in many ways. The main tasks of the classifier are shown in Figure3.Figure 3
The main tasks of the classifier.As shown in Figure3, circles and triangles are used to represent the first type of sample and the second type of sample. Each type of sample is represented by a vector, and the category boundary is represented by an ellipse in the figure [12].In addition, there is a straight line connecting the two categories in the graph. The parameters of the equation include coordinatesx,x2, which are as follows:(3)dx=w1x1+w2x2+w0=0,dx can divide the space into two different decision-making regions, which is called a linear decision function.The weight vector can be set tow=w...wdt, and the generalized linear decision function of the d-dimensional feature space can be obtained as follows:(4)dx=w∗x∗=w1x+w0.


#### 2.1.3. SVM Classifier

SVM is also a support vector machine, which is a two-classification model. It maps the feature vector of the instance to some points in the space. The purpose of SVM is to draw a line to distinguish these two types of points “best.” Even if there are new points in the future, this line can also make a good classification. SVM is suitable for small and medium-sized data samples and nonlinear, high-dimensional classification problems. The goal of SVM operation is to find a more reasonable method to obtain a more suitable classification hyperplane in the high-dimensional feature space. Hyperplane classification can optimize the generalization of understanding of the world. Optimization theory can provide an important mathematical method for hyperplane optimization [13].Assuming that the nonlinear mapping isx⟶ϕx, then the objective function of the dual form is as follows:(5)Qα=∑k=1nαk−12∑j=1n∑j=1nαiαjbibj.It is proposed to replace the inner product operation with a kernel function that satisfies the conditions∑k=1nαk. The optimal discriminant function Qα of the kernel form is as follows:(6)fa=sgn∑xi∈SVαi∗bika∗ai+b∗.


### 2.2. SAR Image Simulation

#### 2.2.1. The Basic Algorithm of SAR Image Simulation

An important way of SAR image processing is to perform edge extraction and image segmentation after coherent speckle noise filtering. The commonly used filtering methods are low-pass filtering, which better keeps the speckle noise and blurs the image at the same time. This includes mean filtering, Gaussian filtering, and so on. SAR target recognition refers to the use of SAR to detect a single target or target group, analyze the information obtained, and determine the target type, model, and other attributes [14]. The target recognition process is shown in Figure 4.Figure 4
Schematic diagram of a typical SAR target recognition system.As shown in Figure4, this layered method makes the amount of calculation more and more large, while the amount of data that needs to be processed is gradually reduced, which is greatly reduced, thereby improving the target recognition system.Compared with matched filter correlator, joint transform correlator has many advantages, such as high spatial bandwidth product, no filtering synthesis, and easy real-time operation. So, in recent years, it has attracted more research interest. The joint transform correlator has a plane that forms a joint input, and the flowchart of joint transform correlation recognition by a computer [15] is shown in Figure 5.Figure 5
Flowchart of the related process of joint transformation.As shown in Figure5, the Fourier transform spectrum realized by the function is located in the upper left corner of the image, so it is also necessary to pay attention during the simulation process. In order to obtain the simulated optical Fourier, in the case of the image center leaf transform, a function that moves the zero frequency component to the center of the spectrum must be called [16].


#### 2.2.2. Coherent Speckle Noise

The coherent speckle noise of synthetic aperture radar (SAR) images seriously reduces the interpretability of the image and affects the subsequent target detection, classification, and recognition applications. The coherent speckle noise of SAR images is a principle defect in the imaging process. Because of the coherent speckle noise, it will have a certain impact on the synthetic aperture radar image. In order to process and image the echo data faster and to identify the target more accurately, multiview processing is usually used to remove [17]. Multiview processing mainly researches the use of geometric methods and restores three-dimensional objects through several multiview geometric two-dimensional images. In short, it is the study of three-dimensional reconstruction, which is mainly used in computer vision. Then, the speckle noise is as follows:(7)ai=δi×ni.Among them, the mean ofq is ni, and σ2 is a measure of spot intensity. The additive noise model related to the signal is equivalent to the following:(8)ai=δi+ni∗,δini−1.Among them,ni∗ is additive noise, and the variance is as follows:(9)varn∗=Eni∗−02=Eai2•σi21+σi2.For the same image,σi2/1+σi2 can be considered as a constant, especially in homogeneous regions. According to the multiplicative model of noise, the noise and the image data are independent of each other and the mean value is 1. As a result, a set of random numbers with a mean value of 1 according to the Rayleigh distribution are generated. Multiplying the random number with the image data can get a SAR simulation image with coherent speckle noise [18].


#### 2.2.3. Multiview Processing

UsingHLR to represent the average of L independent images at position R, the following is obtained:(10)HL=1L∑i=1LHi.The varianceHL−H02 is reduced to the original 1/L as follows:(11)varHL=EHL−H02=1L2×E∑i=1LHL−H02.


### 2.3. Fourier Transform Based on Optical Imaging

#### 2.3.1. Fourier Transform Algorithm

In different research fields, Fourier transform has many different variant forms, such as continuous Fourier transform and discrete Fourier transform. At first, Fourier analysis was proposed as a tool for analytical analysis of thermal processes. The most important theory in information processing is the Fourier transform [19]. It is as follows:(12)Gf=∫−∞∞gaexp−j2πfada,ga=∫−∞∞Gfexp−j2πfadf,Gf is called the Fourier transform of ga, or frequency spectrum. When Gf is a complex function, it can be expressed as follows:(13)Gf=Afexp−jφf,where Af=Gf is the amplitude spectrum of ga [20].The inverse Fourier transformsga and φf called ga as Gf constitute the Fourier transform. It is generalized to the following:(14)Gu,v=∫−∞∞∫−∞∞ga,bexp−j2πua+vbdadb,where u2+v2 represents the spatial frequency after Fourier transform.


#### 2.3.2. Histogram Equalization Enhancement

Histogram equalization is a method in the field of image processing that uses image histograms to adjust contrast. This method is usually used to increase the global contrast of many images, especially when the contrast of the useful data of the image is quite close. In this way, the brightness can be better distributed on the histogram. The histogram equalization refers to the process of converting an image into different histograms using the equalization of gray-scale mapping conversion as follows:(15)Prdi=mim,where m is the total number of pixels, and di satisfies the normalization condition [21].The image transformation function expression is as follows:(16)Si=Tdi=∑i=0k=1pddi=∑i=0k=1mim.Among them,k is the number of gray levels.This is the histogram before and after the histogram equalization (Figure6).Figure 6
Histogram before and after histogram equalization. (a) Histogram before histogram equalization. (b) Histogram after histogram equalization.
(a)(b)As shown in Figure6, it can be seen from the figure that after the histogram is equalized and corrected, the gray interval of the image histogram is enlarged. This helps the analysis and recognition of the image and the use of the equalized histogram to correct the image [22].


#### 2.3.3. Smoothing Filter Enhancement

The following briefly introduces the basic principles of linear smoothing filters [23]. The gray value is Fj,k, and the output corresponding to pixel Fj,k is as follows:(17)Gj,k=1H∑a,b=LFa,b.That is, the average gray value of the window pixels is used to replace the original gray value ofFj,k.If the contrast between the object and the background is clear, the object boundary is located at the highest point of the image gradient. Like the differential emphasis method, the gradient emphasis method is easily affected by noise and deviates from the object boundary [24]. Usually, it needs to smooth the gradation image Gfi,j before emphasizing. For digital images, it is defined as follows:(18)Gfi,j=fai,j2+fbi,j21/2.The energy of the image is mainly concentrated in the low and middle frequencies of the amplitude spectrum, while the edges and noise of the image correspond to the high frequency part. The function of the low-pass filter is to remove these high-frequency components through the filter, maintain low-frequency components, and smooth the image information [25]. The low-pass filter is as follows:(19)Gu,v=Fu,vHu,v.In formula (19), Fu,v is the Fourier transform of the original image Fa,b with noise; Gu,v is the Fourier transform of the image output by the low-pass filter.In areaD0 of a Fourier plane, the transfer function of an ideal low-pass filter is as follows:(20)Hu,v=1,Du,v∈D0,0,Du,v∉D0.From a theoretical analysis, the frequency band in areaD0 passes through without loss, while the high-frequency signal outside the area is filtered out; if a large amount of edge information contained in the high-frequency signal is also filtered out, the phenomenon of image blur will occur.


## 2.1. Target Monitoring and Recognition Technology Algorithm

The interframe difference method is a method of obtaining the contour of a moving target by performing a difference operation on two adjacent frames in a video image sequence. It can be well adapted to the situation where there are multiple moving targets and camera movement. The interframe difference method is a method that uses the relationship between the prestation and poststation in the image sequence to detect the moving area. It is shown in Figure2.Figure 2
Motion between frame differences.As shown in Figure2, when there is abnormal object movement in the surveillance scene, there will be more obvious differences between frames. The two frames are subtracted to obtain the absolute value of the brightness difference of the two frames. It judges whether it is greater than the threshold to analyze the motion characteristics of the video or image sequence and determines whether there is any object movement in the image sequence. The frame-by-frame difference of the image sequence is equivalent to high-pass filtering of the image sequence in the time domain. Discrimination of motion area or background is as follows:(1)Mask1a,b=1,Ita,b≻Td,0,otherwise.In the formula,Td is the discrimination threshold.


### 2.1.1. HOG Features

The calculation formula of gradient strength and direction is as follows:(2)h=Ix2+IY2.Finally, it creates a direction histogram with each cell in the image as a unit.


### 2.1.2. Decision Area and Decision Function

In the field of machine learning, the goal of classification is to gather objects with similar characteristics. A linear classifier makes classification decisions through linear combinations of features to achieve this goal. The characteristics of objects are usually described as eigenvalues, and in vectors, they are described as eigenvectors. The classifier has been applied in many ways. The main tasks of the classifier are shown in Figure3.Figure 3
The main tasks of the classifier.As shown in Figure3, circles and triangles are used to represent the first type of sample and the second type of sample. Each type of sample is represented by a vector, and the category boundary is represented by an ellipse in the figure [12].In addition, there is a straight line connecting the two categories in the graph. The parameters of the equation include coordinatesx,x2, which are as follows:(3)dx=w1x1+w2x2+w0=0,dx can divide the space into two different decision-making regions, which is called a linear decision function.The weight vector can be set tow=w...wdt, and the generalized linear decision function of the d-dimensional feature space can be obtained as follows:(4)dx=w∗x∗=w1x+w0.


### 2.1.3. SVM Classifier

SVM is also a support vector machine, which is a two-classification model. It maps the feature vector of the instance to some points in the space. The purpose of SVM is to draw a line to distinguish these two types of points “best.” Even if there are new points in the future, this line can also make a good classification. SVM is suitable for small and medium-sized data samples and nonlinear, high-dimensional classification problems. The goal of SVM operation is to find a more reasonable method to obtain a more suitable classification hyperplane in the high-dimensional feature space. Hyperplane classification can optimize the generalization of understanding of the world. Optimization theory can provide an important mathematical method for hyperplane optimization [13].Assuming that the nonlinear mapping isx⟶ϕx, then the objective function of the dual form is as follows:(5)Qα=∑k=1nαk−12∑j=1n∑j=1nαiαjbibj.It is proposed to replace the inner product operation with a kernel function that satisfies the conditions∑k=1nαk. The optimal discriminant function Qα of the kernel form is as follows:(6)fa=sgn∑xi∈SVαi∗bika∗ai+b∗.


## 2.1.1. HOG Features

The calculation formula of gradient strength and direction is as follows:(2)h=Ix2+IY2.Finally, it creates a direction histogram with each cell in the image as a unit.


## 2.1.2. Decision Area and Decision Function

In the field of machine learning, the goal of classification is to gather objects with similar characteristics. A linear classifier makes classification decisions through linear combinations of features to achieve this goal. The characteristics of objects are usually described as eigenvalues, and in vectors, they are described as eigenvectors. The classifier has been applied in many ways. The main tasks of the classifier are shown in Figure3.Figure 3
The main tasks of the classifier.As shown in Figure3, circles and triangles are used to represent the first type of sample and the second type of sample. Each type of sample is represented by a vector, and the category boundary is represented by an ellipse in the figure [12].In addition, there is a straight line connecting the two categories in the graph. The parameters of the equation include coordinatesx,x2, which are as follows:(3)dx=w1x1+w2x2+w0=0,dx can divide the space into two different decision-making regions, which is called a linear decision function.The weight vector can be set tow=w...wdt, and the generalized linear decision function of the d-dimensional feature space can be obtained as follows:(4)dx=w∗x∗=w1x+w0.


## 2.1.3. SVM Classifier

SVM is also a support vector machine, which is a two-classification model. It maps the feature vector of the instance to some points in the space. The purpose of SVM is to draw a line to distinguish these two types of points “best.” Even if there are new points in the future, this line can also make a good classification. SVM is suitable for small and medium-sized data samples and nonlinear, high-dimensional classification problems. The goal of SVM operation is to find a more reasonable method to obtain a more suitable classification hyperplane in the high-dimensional feature space. Hyperplane classification can optimize the generalization of understanding of the world. Optimization theory can provide an important mathematical method for hyperplane optimization [13].Assuming that the nonlinear mapping isx⟶ϕx, then the objective function of the dual form is as follows:(5)Qα=∑k=1nαk−12∑j=1n∑j=1nαiαjbibj.It is proposed to replace the inner product operation with a kernel function that satisfies the conditions∑k=1nαk. The optimal discriminant function Qα of the kernel form is as follows:(6)fa=sgn∑xi∈SVαi∗bika∗ai+b∗.


## 2.2. SAR Image Simulation

### 2.2.1. The Basic Algorithm of SAR Image Simulation

An important way of SAR image processing is to perform edge extraction and image segmentation after coherent speckle noise filtering. The commonly used filtering methods are low-pass filtering, which better keeps the speckle noise and blurs the image at the same time. This includes mean filtering, Gaussian filtering, and so on. SAR target recognition refers to the use of SAR to detect a single target or target group, analyze the information obtained, and determine the target type, model, and other attributes [14]. The target recognition process is shown in Figure 4.Figure 4
Schematic diagram of a typical SAR target recognition system.As shown in Figure4, this layered method makes the amount of calculation more and more large, while the amount of data that needs to be processed is gradually reduced, which is greatly reduced, thereby improving the target recognition system.Compared with matched filter correlator, joint transform correlator has many advantages, such as high spatial bandwidth product, no filtering synthesis, and easy real-time operation. So, in recent years, it has attracted more research interest. The joint transform correlator has a plane that forms a joint input, and the flowchart of joint transform correlation recognition by a computer [15] is shown in Figure 5.Figure 5
Flowchart of the related process of joint transformation.As shown in Figure5, the Fourier transform spectrum realized by the function is located in the upper left corner of the image, so it is also necessary to pay attention during the simulation process. In order to obtain the simulated optical Fourier, in the case of the image center leaf transform, a function that moves the zero frequency component to the center of the spectrum must be called [16].


### 2.2.2. Coherent Speckle Noise

The coherent speckle noise of synthetic aperture radar (SAR) images seriously reduces the interpretability of the image and affects the subsequent target detection, classification, and recognition applications. The coherent speckle noise of SAR images is a principle defect in the imaging process. Because of the coherent speckle noise, it will have a certain impact on the synthetic aperture radar image. In order to process and image the echo data faster and to identify the target more accurately, multiview processing is usually used to remove [17]. Multiview processing mainly researches the use of geometric methods and restores three-dimensional objects through several multiview geometric two-dimensional images. In short, it is the study of three-dimensional reconstruction, which is mainly used in computer vision. Then, the speckle noise is as follows:(7)ai=δi×ni.Among them, the mean ofq is ni, and σ2 is a measure of spot intensity. The additive noise model related to the signal is equivalent to the following:(8)ai=δi+ni∗,δini−1.Among them,ni∗ is additive noise, and the variance is as follows:(9)varn∗=Eni∗−02=Eai2•σi21+σi2.For the same image,σi2/1+σi2 can be considered as a constant, especially in homogeneous regions. According to the multiplicative model of noise, the noise and the image data are independent of each other and the mean value is 1. As a result, a set of random numbers with a mean value of 1 according to the Rayleigh distribution are generated. Multiplying the random number with the image data can get a SAR simulation image with coherent speckle noise [18].


### 2.2.3. Multiview Processing

UsingHLR to represent the average of L independent images at position R, the following is obtained:(10)HL=1L∑i=1LHi.The varianceHL−H02 is reduced to the original 1/L as follows:(11)varHL=EHL−H02=1L2×E∑i=1LHL−H02.


## 2.2.1. The Basic Algorithm of SAR Image Simulation

An important way of SAR image processing is to perform edge extraction and image segmentation after coherent speckle noise filtering. The commonly used filtering methods are low-pass filtering, which better keeps the speckle noise and blurs the image at the same time. This includes mean filtering, Gaussian filtering, and so on. SAR target recognition refers to the use of SAR to detect a single target or target group, analyze the information obtained, and determine the target type, model, and other attributes [14]. The target recognition process is shown in Figure 4.Figure 4
Schematic diagram of a typical SAR target recognition system.As shown in Figure4, this layered method makes the amount of calculation more and more large, while the amount of data that needs to be processed is gradually reduced, which is greatly reduced, thereby improving the target recognition system.Compared with matched filter correlator, joint transform correlator has many advantages, such as high spatial bandwidth product, no filtering synthesis, and easy real-time operation. So, in recent years, it has attracted more research interest. The joint transform correlator has a plane that forms a joint input, and the flowchart of joint transform correlation recognition by a computer [15] is shown in Figure 5.Figure 5
Flowchart of the related process of joint transformation.As shown in Figure5, the Fourier transform spectrum realized by the function is located in the upper left corner of the image, so it is also necessary to pay attention during the simulation process. In order to obtain the simulated optical Fourier, in the case of the image center leaf transform, a function that moves the zero frequency component to the center of the spectrum must be called [16].


## 2.2.2. Coherent Speckle Noise

The coherent speckle noise of synthetic aperture radar (SAR) images seriously reduces the interpretability of the image and affects the subsequent target detection, classification, and recognition applications. The coherent speckle noise of SAR images is a principle defect in the imaging process. Because of the coherent speckle noise, it will have a certain impact on the synthetic aperture radar image. In order to process and image the echo data faster and to identify the target more accurately, multiview processing is usually used to remove [17]. Multiview processing mainly researches the use of geometric methods and restores three-dimensional objects through several multiview geometric two-dimensional images. In short, it is the study of three-dimensional reconstruction, which is mainly used in computer vision. Then, the speckle noise is as follows:(7)ai=δi×ni.Among them, the mean ofq is ni, and σ2 is a measure of spot intensity. The additive noise model related to the signal is equivalent to the following:(8)ai=δi+ni∗,δini−1.Among them,ni∗ is additive noise, and the variance is as follows:(9)varn∗=Eni∗−02=Eai2•σi21+σi2.For the same image,σi2/1+σi2 can be considered as a constant, especially in homogeneous regions. According to the multiplicative model of noise, the noise and the image data are independent of each other and the mean value is 1. As a result, a set of random numbers with a mean value of 1 according to the Rayleigh distribution are generated. Multiplying the random number with the image data can get a SAR simulation image with coherent speckle noise [18].


## 2.2.3. Multiview Processing

UsingHLR to represent the average of L independent images at position R, the following is obtained:(10)HL=1L∑i=1LHi.The varianceHL−H02 is reduced to the original 1/L as follows:(11)varHL=EHL−H02=1L2×E∑i=1LHL−H02.


## 2.3. Fourier Transform Based on Optical Imaging

### 2.3.1. Fourier Transform Algorithm

In different research fields, Fourier transform has many different variant forms, such as continuous Fourier transform and discrete Fourier transform. At first, Fourier analysis was proposed as a tool for analytical analysis of thermal processes. The most important theory in information processing is the Fourier transform [19]. It is as follows:(12)Gf=∫−∞∞gaexp−j2πfada,ga=∫−∞∞Gfexp−j2πfadf,Gf is called the Fourier transform of ga, or frequency spectrum. When Gf is a complex function, it can be expressed as follows:(13)Gf=Afexp−jφf,where Af=Gf is the amplitude spectrum of ga [20].The inverse Fourier transformsga and φf called ga as Gf constitute the Fourier transform. It is generalized to the following:(14)Gu,v=∫−∞∞∫−∞∞ga,bexp−j2πua+vbdadb,where u2+v2 represents the spatial frequency after Fourier transform.


### 2.3.2. Histogram Equalization Enhancement

Histogram equalization is a method in the field of image processing that uses image histograms to adjust contrast. This method is usually used to increase the global contrast of many images, especially when the contrast of the useful data of the image is quite close. In this way, the brightness can be better distributed on the histogram. The histogram equalization refers to the process of converting an image into different histograms using the equalization of gray-scale mapping conversion as follows:(15)Prdi=mim,where m is the total number of pixels, and di satisfies the normalization condition [21].The image transformation function expression is as follows:(16)Si=Tdi=∑i=0k=1pddi=∑i=0k=1mim.Among them,k is the number of gray levels.This is the histogram before and after the histogram equalization (Figure6).Figure 6
Histogram before and after histogram equalization. (a) Histogram before histogram equalization. (b) Histogram after histogram equalization.
(a)(b)As shown in Figure6, it can be seen from the figure that after the histogram is equalized and corrected, the gray interval of the image histogram is enlarged. This helps the analysis and recognition of the image and the use of the equalized histogram to correct the image [22].


### 2.3.3. Smoothing Filter Enhancement

The following briefly introduces the basic principles of linear smoothing filters [23]. The gray value is Fj,k, and the output corresponding to pixel Fj,k is as follows:(17)Gj,k=1H∑a,b=LFa,b.That is, the average gray value of the window pixels is used to replace the original gray value ofFj,k.If the contrast between the object and the background is clear, the object boundary is located at the highest point of the image gradient. Like the differential emphasis method, the gradient emphasis method is easily affected by noise and deviates from the object boundary [24]. Usually, it needs to smooth the gradation image Gfi,j before emphasizing. For digital images, it is defined as follows:(18)Gfi,j=fai,j2+fbi,j21/2.The energy of the image is mainly concentrated in the low and middle frequencies of the amplitude spectrum, while the edges and noise of the image correspond to the high frequency part. The function of the low-pass filter is to remove these high-frequency components through the filter, maintain low-frequency components, and smooth the image information [25]. The low-pass filter is as follows:(19)Gu,v=Fu,vHu,v.In formula (19), Fu,v is the Fourier transform of the original image Fa,b with noise; Gu,v is the Fourier transform of the image output by the low-pass filter.In areaD0 of a Fourier plane, the transfer function of an ideal low-pass filter is as follows:(20)Hu,v=1,Du,v∈D0,0,Du,v∉D0.From a theoretical analysis, the frequency band in areaD0 passes through without loss, while the high-frequency signal outside the area is filtered out; if a large amount of edge information contained in the high-frequency signal is also filtered out, the phenomenon of image blur will occur.


## 2.3.1. Fourier Transform Algorithm

In different research fields, Fourier transform has many different variant forms, such as continuous Fourier transform and discrete Fourier transform. At first, Fourier analysis was proposed as a tool for analytical analysis of thermal processes. The most important theory in information processing is the Fourier transform [19]. It is as follows:(12)Gf=∫−∞∞gaexp−j2πfada,ga=∫−∞∞Gfexp−j2πfadf,Gf is called the Fourier transform of ga, or frequency spectrum. When Gf is a complex function, it can be expressed as follows:(13)Gf=Afexp−jφf,where Af=Gf is the amplitude spectrum of ga [20].The inverse Fourier transformsga and φf called ga as Gf constitute the Fourier transform. It is generalized to the following:(14)Gu,v=∫−∞∞∫−∞∞ga,bexp−j2πua+vbdadb,where u2+v2 represents the spatial frequency after Fourier transform.


## 2.3.2. Histogram Equalization Enhancement

Histogram equalization is a method in the field of image processing that uses image histograms to adjust contrast. This method is usually used to increase the global contrast of many images, especially when the contrast of the useful data of the image is quite close. In this way, the brightness can be better distributed on the histogram. The histogram equalization refers to the process of converting an image into different histograms using the equalization of gray-scale mapping conversion as follows:(15)Prdi=mim,where m is the total number of pixels, and di satisfies the normalization condition [21].The image transformation function expression is as follows:(16)Si=Tdi=∑i=0k=1pddi=∑i=0k=1mim.Among them,k is the number of gray levels.This is the histogram before and after the histogram equalization (Figure6).Figure 6
Histogram before and after histogram equalization. (a) Histogram before histogram equalization. (b) Histogram after histogram equalization.
(a)(b)As shown in Figure6, it can be seen from the figure that after the histogram is equalized and corrected, the gray interval of the image histogram is enlarged. This helps the analysis and recognition of the image and the use of the equalized histogram to correct the image [22].


## 2.3.3. Smoothing Filter Enhancement

The following briefly introduces the basic principles of linear smoothing filters [23]. The gray value is Fj,k, and the output corresponding to pixel Fj,k is as follows:(17)Gj,k=1H∑a,b=LFa,b.That is, the average gray value of the window pixels is used to replace the original gray value ofFj,k.If the contrast between the object and the background is clear, the object boundary is located at the highest point of the image gradient. Like the differential emphasis method, the gradient emphasis method is easily affected by noise and deviates from the object boundary [24]. Usually, it needs to smooth the gradation image Gfi,j before emphasizing. For digital images, it is defined as follows:(18)Gfi,j=fai,j2+fbi,j21/2.The energy of the image is mainly concentrated in the low and middle frequencies of the amplitude spectrum, while the edges and noise of the image correspond to the high frequency part. The function of the low-pass filter is to remove these high-frequency components through the filter, maintain low-frequency components, and smooth the image information [25]. The low-pass filter is as follows:(19)Gu,v=Fu,vHu,v.In formula (19), Fu,v is the Fourier transform of the original image Fa,b with noise; Gu,v is the Fourier transform of the image output by the low-pass filter.In areaD0 of a Fourier plane, the transfer function of an ideal low-pass filter is as follows:(20)Hu,v=1,Du,v∈D0,0,Du,v∉D0.From a theoretical analysis, the frequency band in areaD0 passes through without loss, while the high-frequency signal outside the area is filtered out; if a large amount of edge information contained in the high-frequency signal is also filtered out, the phenomenon of image blur will occur.


## 3. Experiment and Analysis of the Investigation of the Capacity Developments

### 3.1. Investigation and Analysis of the Capacity Development Trend

Capacity development index system not only needs to meet theoretical requirements but also needs to be verified through actual data. Therefore, this article takes sports social organizations as an example to test the scientific nature of the index system. This reflects the current situation and problems of the development capabilities of sports social organizations and puts forward relevant policy recommendations. The continuous advancement of the reform and opening policy has greatly promoted the further development of the national economy and at the same time has created a good environment and conditions for the development of sports social organizations. After 1979, sports associations were established in various places and basically every province and city established sports associations. China has always been a benchmark in the development of sports. After the establishment of various associations, it has actively promoted the growth and development of social organizations to more comprehensively and effectively meet the needs of the people to participate in physical exercise. This article investigates the development trend of Chinese sports organizations from 2015 to 2018, as shown in Table1.Table 1
Trends in the development of Chinese sports organizations from 2015 to 2018.
YearSports social groupsSports civil nonorganizationSports foundation2015789435620168753212672017123445327820181343542293As shown in Table1, from 2015 to 2018, the development of sports social organizations increased with the increase of the year, but the magnitude was not high. By 2016, sports social groups have been unable to keep up with the increase in the number of sports nongroups.That is because there is no effective use of the media and holding events to conduct better social publicity, increase the social influence of sports social organizations, attract more people to participate, and improve the active role of sports social organizations in promoting mass fitness.This article investigates the development trend of Chinese sports social organizations from 2019 to 2020, as shown in Figure7.Figure 7
The development trend of Chinese sports social organizations from 2019 to 2020. (a) The development trend of Chinese sports social organization in 2019. (b) The development trend of Chinese sports social organization in 2020.
(a)(b)As shown in Figure7, the development trend of sports social organizations from 2019 to 2020 is getting better and better, and the growth rate is getting higher and higher. At the same time, with the continuous economic development and the improvement of people’s living standards, people’s demands for health are getting higher and higher. They have sufficient economic strength and willingness to participate in certain sports associations, participate in corresponding sports activities, and enjoy relevant sports services. This has promoted the development of sports activities, sports performances, sports events, and the development of sports social organizations and the sports industry. The good development of sports social organizations has provided better and better sports services to the public, thus achieving a virtuous circle.This article also conducted a survey on the social organization of sports in other countries, as shown in Table2.Table 2
Sports social organizations in other countries.
YearSports social groupsSports civil nonorganizationSports foundationCountry A4321742121Country B4214656132Country C387356489Country D376848697Country E345336567As shown in Table2, country A is the most important in terms of sports organizations, reaching 4321 organizations. In developed countries such as Europe and the United States, sports activities are emphasized and integration into daily life has become a way of life. The health consciousness of the masses is very strong, and the most basic requirement for people to participate in sports activities is to improve the quality of their health. Meeting this demand is based on the premise of continuous and regular participation in sports activities. Therefore, various social sports organizations came into being and developed rapidly.This article also investigated the situation of the main personnel of a certain sports organization, as shown in Table3.Table 3
The situation of the main personnel of a sports organization.
AgeUnder 1818–30 years old30–42 years old42–54 years old54–66 years oldQuantity64325676476Percentage (%)132.442.61113As shown in Table3, the age of the personnel of this sports organization is generally within the age of 18–66, and the number of participants between the ages of 30–42 is the largest, accounting for 42.6%. It can be seen that middle-aged people are more likely to refer to sports organizations, and they pay more attention to sports than other ages.This article conducted a rough survey of the members of the five sports organizations surveyed, as shown in Table4.Table 4
Five memberships of sports organizations.
Survey object12345EducationUndergraduateJunior collegeUndergraduateJunior collegeHigh schoolAge18–6018–6015–6517–6418–65Hobby (%)7075737472As shown in Table4, the members of the five sports organizations have a high school education or above, and two organizations have a bachelor’s degree or above; the overall age range is between 15 and 65 years old, and the degree of interest in sports is more than 70%. It can be seen that the differences between the five sports organizations are not significant.Running was the most important activity type among the five sports social organizations that were randomly selected and investigated. It is shown in Table5.Table 5
Main types of activities of sports social organizations.
Sports social organizationRunningBadmintonMartial artsPingpong156463227254453225357473424453433527555463128As shown in Table5, people usually like to organize activities such as running, badminton, martial arts, and table tennis. Among them, running is the most popular among sports organizations, and the average number of runs selected by the five sports organizations is about 55 times.This question investigates the factors that affect the development of sports social organizations, as shown in Figure8.Figure 8
Factors affecting the capacity developments.As shown in Figure8, the main factors affecting are age, income, whether it love sports, personality, education, and physical fitness. Among them, experts believe that the most influential factor in the development of sports social organizations is whether they love sports, because talents who love sports will actively organize activities to participate in activities.In summary, the factors affecting the development of sports organizations are as follows.


#### 3.1.1. Income

People between the ages of 18–45 are the pillars of social development, and they are the most stressful part of social life. These people most need a strong body to face life but also need to release life and work stress through exercise. However, people with high monthly incomes need to pursue spiritual life and healthy body after solving the food and clothing in life. Therefore, more people will choose to exercise their bodies through sports to strengthen their physique and release stress, while also expanding their social spheres and delighting the body and mind.


#### 3.1.2. The Quality of the Person in Charge of the Organization

The management system of more than half of sports social organizations adopts the responsibility system of the person in charge of the organization, and the person in charge of the organization determines the organization’s decision-making and the arrangement of the activity plan. A correct organizational decision or a perfect event arrangement is not only responsible for the organization itself but also responsible for the members of the organization. Persons in charge with higher quality are often able to make correct organizational decisions or arrangements for activities. Therefore, high-quality responsible persons with good management capabilities are essential to the development of sports social organizations.


#### 3.1.3. The Enthusiasm of the Organization Members to Exercise

The rapid development of sports social organizations is rooted in people’s extensive fitness needs; the promotion of national fitness policies especially in recent years has set off an upsurge of national fitness. This makes various sports and fitness organizations emerge in endlessly while meeting the huge fitness needs of the people. In the past two years, people’s fitness demand has shown a rapid upward trend, and the rapid growth of people’s demand for sports fitness has also brought various sports social organizations into a period of rapid development. People’s enthusiasm for physical exercise is soaring, which has given birth to more sports social organizations.


### 3.2. Measures to Promote the Development of Sports Organizations

(1)
It is necessary to promote a strong national awareness of fitness and willingness to participate in sports activities. It needs to promote the development of sports social organizations. The development of sports social organizations has promoted the development of nationwide fitness activities and provided people with better and more comprehensive sports public services.(2)
The degree of socialization is relatively high. Various sports organizations in some economically developed countries have achieved a certain degree of development without government funds. These are generally funds provided by enterprises or benefit from the development of mass sports. Various sports associations, clubs, and other sports organizations provide individuals with various sports opportunities to meet individual needs. This improves people’s sports ability and interest in sports and ultimately makes sports activities a part of life.(3)
Mass sports promotes the development of the sports industry. In Europe and the United States and other countries, private clubs based on profit give mass sports activities a strong vitality. These clubs vary in size and are widely distributed. Through sports activities, the physical fitness of the masses has been enhanced, and many people have voluntarily joined the ranks of “spending money for health.” People attach importance to healthy life, actively join various sports associations and sports clubs, and participate in various sports and fitness activities, which greatly promotes the development of sports service industry, sports goods industry, and other related sports industries.


## 3.1. Investigation and Analysis of the Capacity Development Trend

Capacity development index system not only needs to meet theoretical requirements but also needs to be verified through actual data. Therefore, this article takes sports social organizations as an example to test the scientific nature of the index system. This reflects the current situation and problems of the development capabilities of sports social organizations and puts forward relevant policy recommendations. The continuous advancement of the reform and opening policy has greatly promoted the further development of the national economy and at the same time has created a good environment and conditions for the development of sports social organizations. After 1979, sports associations were established in various places and basically every province and city established sports associations. China has always been a benchmark in the development of sports. After the establishment of various associations, it has actively promoted the growth and development of social organizations to more comprehensively and effectively meet the needs of the people to participate in physical exercise. This article investigates the development trend of Chinese sports organizations from 2015 to 2018, as shown in Table1.Table 1
Trends in the development of Chinese sports organizations from 2015 to 2018.
YearSports social groupsSports civil nonorganizationSports foundation2015789435620168753212672017123445327820181343542293As shown in Table1, from 2015 to 2018, the development of sports social organizations increased with the increase of the year, but the magnitude was not high. By 2016, sports social groups have been unable to keep up with the increase in the number of sports nongroups.That is because there is no effective use of the media and holding events to conduct better social publicity, increase the social influence of sports social organizations, attract more people to participate, and improve the active role of sports social organizations in promoting mass fitness.This article investigates the development trend of Chinese sports social organizations from 2019 to 2020, as shown in Figure7.Figure 7
The development trend of Chinese sports social organizations from 2019 to 2020. (a) The development trend of Chinese sports social organization in 2019. (b) The development trend of Chinese sports social organization in 2020.
(a)(b)As shown in Figure7, the development trend of sports social organizations from 2019 to 2020 is getting better and better, and the growth rate is getting higher and higher. At the same time, with the continuous economic development and the improvement of people’s living standards, people’s demands for health are getting higher and higher. They have sufficient economic strength and willingness to participate in certain sports associations, participate in corresponding sports activities, and enjoy relevant sports services. This has promoted the development of sports activities, sports performances, sports events, and the development of sports social organizations and the sports industry. The good development of sports social organizations has provided better and better sports services to the public, thus achieving a virtuous circle.This article also conducted a survey on the social organization of sports in other countries, as shown in Table2.Table 2
Sports social organizations in other countries.
YearSports social groupsSports civil nonorganizationSports foundationCountry A4321742121Country B4214656132Country C387356489Country D376848697Country E345336567As shown in Table2, country A is the most important in terms of sports organizations, reaching 4321 organizations. In developed countries such as Europe and the United States, sports activities are emphasized and integration into daily life has become a way of life. The health consciousness of the masses is very strong, and the most basic requirement for people to participate in sports activities is to improve the quality of their health. Meeting this demand is based on the premise of continuous and regular participation in sports activities. Therefore, various social sports organizations came into being and developed rapidly.This article also investigated the situation of the main personnel of a certain sports organization, as shown in Table3.Table 3
The situation of the main personnel of a sports organization.
AgeUnder 1818–30 years old30–42 years old42–54 years old54–66 years oldQuantity64325676476Percentage (%)132.442.61113As shown in Table3, the age of the personnel of this sports organization is generally within the age of 18–66, and the number of participants between the ages of 30–42 is the largest, accounting for 42.6%. It can be seen that middle-aged people are more likely to refer to sports organizations, and they pay more attention to sports than other ages.This article conducted a rough survey of the members of the five sports organizations surveyed, as shown in Table4.Table 4
Five memberships of sports organizations.
Survey object12345EducationUndergraduateJunior collegeUndergraduateJunior collegeHigh schoolAge18–6018–6015–6517–6418–65Hobby (%)7075737472As shown in Table4, the members of the five sports organizations have a high school education or above, and two organizations have a bachelor’s degree or above; the overall age range is between 15 and 65 years old, and the degree of interest in sports is more than 70%. It can be seen that the differences between the five sports organizations are not significant.Running was the most important activity type among the five sports social organizations that were randomly selected and investigated. It is shown in Table5.Table 5
Main types of activities of sports social organizations.
Sports social organizationRunningBadmintonMartial artsPingpong156463227254453225357473424453433527555463128As shown in Table5, people usually like to organize activities such as running, badminton, martial arts, and table tennis. Among them, running is the most popular among sports organizations, and the average number of runs selected by the five sports organizations is about 55 times.This question investigates the factors that affect the development of sports social organizations, as shown in Figure8.Figure 8
Factors affecting the capacity developments.As shown in Figure8, the main factors affecting are age, income, whether it love sports, personality, education, and physical fitness. Among them, experts believe that the most influential factor in the development of sports social organizations is whether they love sports, because talents who love sports will actively organize activities to participate in activities.In summary, the factors affecting the development of sports organizations are as follows.


### 3.1.1. Income

People between the ages of 18–45 are the pillars of social development, and they are the most stressful part of social life. These people most need a strong body to face life but also need to release life and work stress through exercise. However, people with high monthly incomes need to pursue spiritual life and healthy body after solving the food and clothing in life. Therefore, more people will choose to exercise their bodies through sports to strengthen their physique and release stress, while also expanding their social spheres and delighting the body and mind.


### 3.1.2. The Quality of the Person in Charge of the Organization

The management system of more than half of sports social organizations adopts the responsibility system of the person in charge of the organization, and the person in charge of the organization determines the organization’s decision-making and the arrangement of the activity plan. A correct organizational decision or a perfect event arrangement is not only responsible for the organization itself but also responsible for the members of the organization. Persons in charge with higher quality are often able to make correct organizational decisions or arrangements for activities. Therefore, high-quality responsible persons with good management capabilities are essential to the development of sports social organizations.


### 3.1.3. The Enthusiasm of the Organization Members to Exercise

The rapid development of sports social organizations is rooted in people’s extensive fitness needs; the promotion of national fitness policies especially in recent years has set off an upsurge of national fitness. This makes various sports and fitness organizations emerge in endlessly while meeting the huge fitness needs of the people. In the past two years, people’s fitness demand has shown a rapid upward trend, and the rapid growth of people’s demand for sports fitness has also brought various sports social organizations into a period of rapid development. People’s enthusiasm for physical exercise is soaring, which has given birth to more sports social organizations.


## 3.1.1. Income

People between the ages of 18–45 are the pillars of social development, and they are the most stressful part of social life. These people most need a strong body to face life but also need to release life and work stress through exercise. However, people with high monthly incomes need to pursue spiritual life and healthy body after solving the food and clothing in life. Therefore, more people will choose to exercise their bodies through sports to strengthen their physique and release stress, while also expanding their social spheres and delighting the body and mind.


## 3.1.2. The Quality of the Person in Charge of the Organization

The management system of more than half of sports social organizations adopts the responsibility system of the person in charge of the organization, and the person in charge of the organization determines the organization’s decision-making and the arrangement of the activity plan. A correct organizational decision or a perfect event arrangement is not only responsible for the organization itself but also responsible for the members of the organization. Persons in charge with higher quality are often able to make correct organizational decisions or arrangements for activities. Therefore, high-quality responsible persons with good management capabilities are essential to the development of sports social organizations.


## 3.1.3. The Enthusiasm of the Organization Members to Exercise

The rapid development of sports social organizations is rooted in people’s extensive fitness needs; the promotion of national fitness policies especially in recent years has set off an upsurge of national fitness. This makes various sports and fitness organizations emerge in endlessly while meeting the huge fitness needs of the people. In the past two years, people’s fitness demand has shown a rapid upward trend, and the rapid growth of people’s demand for sports fitness has also brought various sports social organizations into a period of rapid development. People’s enthusiasm for physical exercise is soaring, which has given birth to more sports social organizations.


## 3.2. Measures to Promote the Development of Sports Organizations

(1)
It is necessary to promote a strong national awareness of fitness and willingness to participate in sports activities. It needs to promote the development of sports social organizations. The development of sports social organizations has promoted the development of nationwide fitness activities and provided people with better and more comprehensive sports public services.(2)
The degree of socialization is relatively high. Various sports organizations in some economically developed countries have achieved a certain degree of development without government funds. These are generally funds provided by enterprises or benefit from the development of mass sports. Various sports associations, clubs, and other sports organizations provide individuals with various sports opportunities to meet individual needs. This improves people’s sports ability and interest in sports and ultimately makes sports activities a part of life.(3)
Mass sports promotes the development of the sports industry. In Europe and the United States and other countries, private clubs based on profit give mass sports activities a strong vitality. These clubs vary in size and are widely distributed. Through sports activities, the physical fitness of the masses has been enhanced, and many people have voluntarily joined the ranks of “spending money for health.” People attach importance to healthy life, actively join various sports associations and sports clubs, and participate in various sports and fitness activities, which greatly promotes the development of sports service industry, sports goods industry, and other related sports industries.


## 4. Discussion

This paper analyzes how to optimize the research on the development ability index system of sports social organizations based on image optimization and recognition simulation. The related concepts of image optimization recognition and the capacity developments are expounded, and the relevant theories based on image optimization recognition and the capacity developments are studied. It explores the method of optimizing the index system of the capacity developments and discusses the influencing factors of the capacity developments through investigating method cases. In the end, it takes the optimization of the index system of the capacity developments and the integration of image optimization recognition simulation technology as an example to explore the correlation between the two.This paper also makes reasonable use of the target detection and recognition technology algorithm based on image optimization and recognition simulation and the basic algorithm of SAR image simulation. With the wider application of the two algorithms, people are paying more and more attention to them, and many scholars have begun to apply these two algorithms to their lives.This article concluded through experiments the following. With the development of society, people pay more and more attention to sports, and the capacity developments also need to be improved. Although there are many factors that affect the capacity developments, the research on optimizing the index system of the capacity developments is very important for sports.


## 5. Conclusions

The focus of this article is the optimization of the index system of the capacity developments that rely on image optimization and recognition simulation. The description is based on the two keywords of image optimization and recognition and the capacity developments. In the method part, this paper proposes the target detection and recognition technology algorithm and the basic algorithm of SAR image simulation based on image optimization and recognition simulation. It uses these two methods to illustrate the simulation of image recognition. The experiment part analyzes the development trend of sports organizations and finds that the demand for sports organizations has been increasing in recent years. This also means that the capacity developments should be correspondingly improved develop sports ability. The main factors that affect the development of sports organizations are the age, quality, educational background, and income of the members of the organization. It must adopt an inclusive and encouraging attitude towards sports social groups and actively participate in national fitness service activities. The research on the optimization of the index system of the capacity developments involves many fields, but the author’s knowledge is limited to the analysis of this article. Research on the optimization of the index system of the capacity developments is very meaningful.



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*Source: 5638797-2022-05-13.xml*