# Artificial Intelligence and VR Environment Design of Digital Museum Based on Embedded Image Processing

Authors: Weiyue Bao; Hong Zhang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

This article first introduces the development of embedded image processing system, puts forward the overall design of the system structure, constructs the development environment of software system and hardware, and introduces in detail the choice of embedded image processing system and artificial intelligence embedded operating system in the industry, the choice of objective lens, and the choice and installation of the light source in the system. According to the system requirements, the design, function, and working method of the GPIO peripheral circuit are introduced in detail, and the circuit diagram and PCB diagram of the GPIO peripheral expansion circuit are provided. Based on this, this article focuses on the technical realization of 3D landscape modeling technology, 3D landscape rotation technology, digital museum dynamic scene fusion technology, interactive animation perception over time, real visual virtual landscape, and virtual roaming scene perception in detail. The 3D landscape modeling technology and the modeling method of digital museum environment in virtual reality in this article are used to analyze and study the general concept of VR environment design in virtual reality environment and make it based on different characteristic methods of different modeling. The overall structure and theoretical basis of the VR environment in the digital museum are based on technical solutions, such as process modeling and scene structure. This technical solution has been verified in practical applications and is achievable. Regarding image recording, this paper proposes an improved image-based recording method. In terms of image coupling, this article uses an improved progressive input and output algorithm that can use CRML to create simple dynamic scenes, which can create clear and vivid animation effects, so that the viewer has a good experience.


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## Body

## 1. Introduction

Although VR technology was developed in the late century, it involves the research and application of many high-tech fields, combines technologies from multiple disciplines, and has excellent performance in various fields such as education, entertainment, aerospace, and military development. Nowadays, the loss of history and cultural heritage is more serious. In order to protect, research, and disseminate it, many countries are actively building digital museums. These museums represent their own history and culture, and the protection and digitization of cultural heritage have become an important research topic in various countries.This paper examines the research status of embedded image processing systems at home and abroad and proposes an embedded image processing system that combines internal technology and artificial intelligence technology to determine the surface contamination detection and processing system of the NVDIA Jetson TK1 development platform. The system uses the ARM Cortex-A15 processor as the core, the Linux+OpenCV viewing library as the software medium, and the blue bar-shaped LED light in the built-in recovery system as the light source. A 500 W pixel Basler industrial camera was used to collect the surface image of the rod. The NVDIA Jetson TK1 development platform uses an algorithm to measure density circularity to detect and identify defects in the rod shape and uses the amount of gray images to determine contamination. Analyze and compare the advantages of each version of embedded system and built-in operating system, propose a general system design scheme suitable for NVIDA Jetson TK1 supercomputer development board as a hardware platform, design a low-cost software environment, and use Linux+OpenCV to view the library which serves as the data center of the embedded image processing system. This article is based on the analysis and implementation of the “Provincial Digital Museum” project. The most important part of the VR system is its interaction, but what it needs is the participation of people, that is, the development and construction of people and the service of people. Its main characteristics have three points: first, effectiveness, that is, the ability of people to provide instructions; second, real-time, that is, the need for feedback to meet the requirements in real time; and third, accuracy, that is, people can give instructions to determine the correct feedback.This article relies on the combination of VRML and Java to control the VRML pattern to complete the complex plan designed around the VR environment. In response to the specific requirements of roaming in virtual museums on the Internet, this paper proposes a virtual platform to display roaming scenes. The platform supports HTML5 browsers, and dynamic scenes must be displayed on this platform in real time, so that users can achieve good interactive digital roaming in the virtual scenes of the museum.


## 2. Related Work

The literature introduced many commonly used scene modeling methods and found a modeling method suitable for this thesis, which can meet the special needs of museum scene exploration [1]. The overall framework established by the virtual museum is provided by several technical solutions of the virtual museum, including the modeling process and the organizational structure of the stage. The literature describes a platform for real-time display of virtual museums [2]. The platform uses a compatible browser and can know the roaming situation of the platform, so as to achieve the purpose of system design. The literature introduces the development of embedded image processing systems, puts forward the overall design of the system structure, establishes a good environment for system software and hardware development, and introduces in detail the choice of embedded systems and integrated operating systems and the choice of industrial cameras and video cameras [3], lens and system selection and installation of light sources, etc. According to the system requirements, the design, function, and working method of the GPIO peripheral circuit are introduced in detail, and the circuit diagram and PCB diagram of the GPIO peripheral expansion circuit are provided [4]. The literature describes the method of selecting cameras and industrial lenses, as well as the method of selecting light sources and bulbs, so as to provide the system with the hardware required to obtain high-quality images. Finally, the blue LED light source is used to improve the shape and contour of the ice cream bar image, thereby reducing the design complexity of the image processing algorithm [5]. The literature introduces the graphic materials of ancient textile machines, restores the 3D animation of the structure to its shape and workflow, and introduces the museum’s digital database for conservation, which is much more vivid than the usual 3D model display.


## 3. Embedded Image Processing and VR Environment Design Model

### 3.1. Embedded Image Processing Technology

#### 3.1.1. Embedded Technology

The embedded technology has good prospects for development, and it is a complete computer system. It combines software and hardware and can run independently. At the same time, the embedded system is also a kind of “device” program; it can be integrated into a specific embedded environment, through assembly and configuration of “smart” controls to control specific facilities and equipment, in order to achieve the purpose of “smart” [6]. The device integrated with the built-in system has better portability and speed.This makes it able to capture more target functions and improves the embedded system. These advantages make it have more and more extensive application fields.Embedded system is a computer system that combines software and hardware and can run independently. It is mainly composed of a built-in operating system, peripheral access devices, a built-in processor, and advanced user software. Hardware and software are the two basic components of the built-in system. The hardware tools lay the foundation of the platform for executing the software functions of the embedded system; the built-in operating system and related implementation software are the core of the entire system, controlling and providing appropriate human-computer interaction services.


#### 3.1.2. Image Processing Model

In MXN, any image can be regarded as a two-dimensional matrix. After transforming the wavelet, the image will be divided into 4 parts of equal size. If you want to expand the second corrugated layer, you must select the low-frequency component (CA1) obtained after the corrugated layer to obtain the short frequency and the second layer.An example of a noise-contaminated image model is(1)pi,j=fi,j+ni,j.After wavelet decomposition, the results are as follows:(2)Wpi,j=Wfi,j+Wni,j.It can be simplified as follows:(3)Pi,j=Fi,j+Ni,j.After obtaining the wave threshold after the image-modified wave coefficient is obtained, it is necessary to select an appropriate threshold and develop a reasonable threshold function to process the wave coefficient to obtain an estimated value,Fi,jtoFˆi,j, where the threshold value divides the wave coefficient into different parts, and the different parts are set to zero, which are maintained or shortened by the threshold function. Therefore, the selection of the threshold and the movement of the threshold are important parts of the defluctuation threshold.Commonly used threshold functions are divided into hard threshold functions and soft threshold functions.The hard threshold function is(4)Pi,j=Pi,jPi,j≥Thr,0Pi,j<Thr.The soft threshold function is(5)Pi,j=sgnPi,jPi,j−ThrPi,j≥Thr,0Pi,j<Thr.The Wiener filter is(6)Pi,j=σf2σf2+σn2Pi,j.Zi,j can be calculated by the following formula:
(7)Zi,j=19∑k,le0,±/Yi−k,j−l.Among them,Yi,j is the wavelet subband coefficient after wavelet attenuation; the value of k,j is 0 and plus or minus 1; that is, the wavelet coefficients of nine points around the point (i,j) are calculated to obtain the context value.After obtaining the context value, the intrinsic relationship between the context value and the fluctuation coefficient must also be found [7]. We have performed two-layer and three-layer wave decomposition in the noisy Lena image and obtained the horizontal wavelet coefficients after the two-layer and three-layer wave decomposition and calculated the Zi,j context value of each position view. Using the context value Zi,j and the fluctuation coefficient Yi,j as the x-coordinate and y-coordinate, draw an image with the relationship between the context value and the fluctuation coefficient in pairs and use the relationship diagram shown.In wavelet denoising, we always use Bayesian threshold as the wavelet denoising threshold. The Bayesian threshold is calculated by the following formula:(8)Thr=σn2σf.It can be seen from the formula that the calculation of the Bayesian threshold first needs to effectively estimate the value of the signal energyσf2 and the standard value of the noise deviation σn2 to obtain the wave coefficient Pi,j, which is a typical signal and image noise mixing matrix of waveforms. The noise is considered to be an independent and given Gaussian white noise that satisfies 2ni,j~N0,σn, and since the wavelet attenuation is orthogonal, the wavelength coefficient Pi,j is the image signal and the total power of the noise signal, as shown in the following equation:
(9)VarP=σf2+σn2.Generally, the standard deviationσn of the noise signal is unknown, and the most commonly used method is to estimate the high-frequency subband through the high-wave coefficient after wave decomposition. The standard deviation σn of the noise signal can be obtained by the following formula:
(10)σn=σ^n=medianYi,j0.6745.Among them,Yi,j is a part of the high-frequency subband after wavelet decomposition. Since the scale space is the same, the degree of noise pollution is the same as the wave decomposition coefficient matrix, so for the same subband, only the standard noise distance needs to be estimated. It can be seen from the above two formulas that the energy value of the image signal is
(11)σf2=VarYi,j−σn2.The context model is used to calculate the phase correlation between wave troughs, and the gap is optimized for wave coefficients of different scale groups and spatial directions [8]. At the same time, according to the optimized wavelet threshold, the optimized Wiener filter function is used as the wavelet threshold function, and the Wiener threshold function is used to create an image noise denoising algorithm. Because the improved wave threshold denoising method has a more concentrated threshold function, it can better preserve the edge details and denoising effect.The improved wave threshold denoising function is(12)Pi,j=σf2σf2+σn2Pi,jPi,j≥Thr,0Pi,j<Thr.0


#### 3.1.3. Simulation Analysis

In order to verify the effect of the improved wave threshold denoising algorithm, a standard512×512 pixel Lena image was added and denoised, and the algorithm was compared with soft threshold algorithm, hard threshold algorithm, and Bayesian threshold. Research through subjective evaluation and objective evaluation is performed to evaluate the impact of image denoising.First, add Gaussian white noise with a standard deviationσn equal to 10-60 to the Lena standard image, then implement the three-wave decomposition of the orthogonal wave based on the sym4 of the noise image, and apply different denoising algorithms for denoising. The comparison is given in Tables 1 and 2. The calculation formula of the average value of square error MSE and maximum signal-to-noise ratio PSNR is as follows:
(13)MSE=1M×N∑i=1M∑j=1NFi,j−F∧i,j2,PSNR=10×log102552×M×N∑i=1∑j=lFi,j−Fi,j.Table 1
Comparison table of MSE values.
Noise standard deviation102030405060Noisy image99.988389.704825.7561359.3561931.7752494.355Soft threshold algorithm71.28783.00393.422105.869121.733144.137Hard threshold algorithm51.51970.97991.535113.559135.810161.603Bayes threshold algorithm51.61970.36289.478109.703130.500155.339Algorithm22.68844.43263.94685.035108.193134.917Table 2
Peak signal-to-noise ratio (PSNR) value comparison table.
Noise standard deviation102030405060Noisy image28.13122.22318.96216.79715.27114.161Soft threshold algorithm29.60128.94028.42627.88327.27726.543Hard threshold algorithm31.01129.62028.51527.57926.80126.046Bayes threshold algorithm31.00329.65728.61427.72926.97526.218Algorithm34.57331.65430.07328.83527.78926.830Among them,Fi,j is the gray value of the original image at point (i,j); it is denoised by M and N. Both of them are valued from the number of rows and columns in the image.It can be seen from two or more tables below the same noise level that the improved method of denoising threshold conforms to the context model, and the effect of Wiener filtering is much better than other comparison algorithms. The peak signal after denoising has been greatly improved. It can also be seen from the table that the mean square method in this paper is also smaller than that in other algorithms. According to objective evaluation criteria, it is determined that the improved method has better results [9–12].The literature proposes a method to denoise contour images based on the context model. The algorithm combines the models under the condition of changing the contour and calculates an appropriate noise reduction threshold according to its distribution characteristics to obtain a good noise reduction effect. Compare the algorithm in this article with this algorithm, and calculate the peak signal-to-noise ratio under different noise levels. The results are shown in Table3.Table 3
Comparison table of PSNR value between the algorithm in this paper and the algorithm in literature.
Noise standard deviation1015202530Algorithms in the literature31.1529.8529.0527.9927.26Algorithm34.5632.8131.6730.7830.05


### 3.2. VR Environment Design Model

The so-called image recording is to find the conversion relationship between several images. The images must meet the same area, especially the same scene, and then match the images. The success of image merging, the success of the algorithm, and the speed of the algorithm directly depend on the image recording, so the image recording may be the most important part of the entire image acquisition process.Mathematically, an image is defined as follows: the reference image and the image to be recorded represent each other, and the registration relationship of the following two images is as follows:(14)I2x,y=gI1fx,y.In the mathematical explanation, it can be seen from the principle of image recording that the more accurate the image recording, the better the conversion between the two images. The change here refers to the relationship between the change of the position and the change of the person, and the record of the final image is known. It is usually not necessary to know its value, so the task of recording the image is to make the most of it. Therefore, equation (14) can be simplified by equation (15).
(15)I2x,y=I1fx,y.The change is a strict change to the rigid body. Rigid body modification models are usually used to record images containing rigid objects such as cars and buildings. It can be expressed by the following formula:(16)x′y′=cosθsinθ−sinθcosθxy+txty.If the object in the combined image not only shows movement, translation, and rotation but also has zooming movement, that is, the size of the object changes, but the shape does not change, then this is a change or a similar change. The uniformity change is expressed by the following formula:(17)x′y′=scosθsinθ−sinθcosθxy+txty.This article uses this pattern to change the recording and stitching of images. Before and after conversion, the line position of the image remains unchanged, and the rest can be changed, such as the shape and size. Therefore, this conversion can satisfy most of the images that are usually collected. This article will use this conversion and behave as the following formula:(18)x′y′=a1a2a3a4xy+txty.In this change, unlike the affine change mode, the linear position relationship before and after the change changes from the initial alignment of the intersection point, and the position change between the spatial images or the movement of the zoom ratio can be expressed as(19)x′y′z′=m0m1m2m3m4m5m6m7m8xyw.The parameters of the formula are expressed bymi, and mii is the 8 degrees of freedom of the matrix, expressed from m0 to m7.
(20)x′=m0x+m1y+m2m6x+m7y+m8,y′=m3x+m4y+m5m6x+m7y+m8.The recording method mainly includes two different methods: methods based on Fourier transform and wavelet transform. In the Fourier transform, the position of the image is changed and is not disturbed by external noise. However, the application of this change in the field of image recording also has disadvantages. For example, it can only record partial images, and the attributes of these images are grayscale with associated wheels. In the domain transform, the Fourier transform method is the most classic.These two images are the reference image and the target image. We assume that the result of the change isG1u,v and G2u,v and obtain the following formula according to the relevant Fourier change:
(21)Φu,v=G1u,vG2∗u,v.The correlation function of the spatial domain obtained by the inverse Fourier transform of the domain formula is as follows:(22)Φu,v=∑u=0M−1∑u=0M−1G1u,vG2∗u,vωM−ujωM−vj.The direct mediation method uses the pixel gray value obtained from the coverage area of the recorded image, and the steps are as follows: find the average value and use the average value to merge the images. The algorithm will filter low-pass images, and the security images will appear banded, which directly leads to a decrease in the quality of the combined images. The method is expressed as follows; the two combined images are denoted byf and f, respectively; and the fused image is denoted by f:
(23)fx,y=f1x,yx,y∈f1,f1x,y+f2x,y2x,y∈f1∩f2,f2x,yx,y∈f2.Combining multiple images involves the same principle. At this time, the overlapping area can be expressed as(24)fx,y=1n∑ifix,y,wherex,y∈f1∩f2∩⋯fn.The last step of the algorithm is to obtain the corner point and determine the position of the corner point by calculating the maximum local value (that is, the response of the first corner point) [13].The algorithm has a faster calculation speed, is more accurate when finding corners, and is not interfered by noise. Therefore, researchers in many fields have carefully studied the algorithm, but it is difficult to know the standardt and g when setting a specific value, and it is difficult to detect edge corners.


## 3.1. Embedded Image Processing Technology

### 3.1.1. Embedded Technology

The embedded technology has good prospects for development, and it is a complete computer system. It combines software and hardware and can run independently. At the same time, the embedded system is also a kind of “device” program; it can be integrated into a specific embedded environment, through assembly and configuration of “smart” controls to control specific facilities and equipment, in order to achieve the purpose of “smart” [6]. The device integrated with the built-in system has better portability and speed.This makes it able to capture more target functions and improves the embedded system. These advantages make it have more and more extensive application fields.Embedded system is a computer system that combines software and hardware and can run independently. It is mainly composed of a built-in operating system, peripheral access devices, a built-in processor, and advanced user software. Hardware and software are the two basic components of the built-in system. The hardware tools lay the foundation of the platform for executing the software functions of the embedded system; the built-in operating system and related implementation software are the core of the entire system, controlling and providing appropriate human-computer interaction services.


### 3.1.2. Image Processing Model

In MXN, any image can be regarded as a two-dimensional matrix. After transforming the wavelet, the image will be divided into 4 parts of equal size. If you want to expand the second corrugated layer, you must select the low-frequency component (CA1) obtained after the corrugated layer to obtain the short frequency and the second layer.An example of a noise-contaminated image model is(1)pi,j=fi,j+ni,j.After wavelet decomposition, the results are as follows:(2)Wpi,j=Wfi,j+Wni,j.It can be simplified as follows:(3)Pi,j=Fi,j+Ni,j.After obtaining the wave threshold after the image-modified wave coefficient is obtained, it is necessary to select an appropriate threshold and develop a reasonable threshold function to process the wave coefficient to obtain an estimated value,Fi,jtoFˆi,j, where the threshold value divides the wave coefficient into different parts, and the different parts are set to zero, which are maintained or shortened by the threshold function. Therefore, the selection of the threshold and the movement of the threshold are important parts of the defluctuation threshold.Commonly used threshold functions are divided into hard threshold functions and soft threshold functions.The hard threshold function is(4)Pi,j=Pi,jPi,j≥Thr,0Pi,j<Thr.The soft threshold function is(5)Pi,j=sgnPi,jPi,j−ThrPi,j≥Thr,0Pi,j<Thr.The Wiener filter is(6)Pi,j=σf2σf2+σn2Pi,j.Zi,j can be calculated by the following formula:
(7)Zi,j=19∑k,le0,±/Yi−k,j−l.Among them,Yi,j is the wavelet subband coefficient after wavelet attenuation; the value of k,j is 0 and plus or minus 1; that is, the wavelet coefficients of nine points around the point (i,j) are calculated to obtain the context value.After obtaining the context value, the intrinsic relationship between the context value and the fluctuation coefficient must also be found [7]. We have performed two-layer and three-layer wave decomposition in the noisy Lena image and obtained the horizontal wavelet coefficients after the two-layer and three-layer wave decomposition and calculated the Zi,j context value of each position view. Using the context value Zi,j and the fluctuation coefficient Yi,j as the x-coordinate and y-coordinate, draw an image with the relationship between the context value and the fluctuation coefficient in pairs and use the relationship diagram shown.In wavelet denoising, we always use Bayesian threshold as the wavelet denoising threshold. The Bayesian threshold is calculated by the following formula:(8)Thr=σn2σf.It can be seen from the formula that the calculation of the Bayesian threshold first needs to effectively estimate the value of the signal energyσf2 and the standard value of the noise deviation σn2 to obtain the wave coefficient Pi,j, which is a typical signal and image noise mixing matrix of waveforms. The noise is considered to be an independent and given Gaussian white noise that satisfies 2ni,j~N0,σn, and since the wavelet attenuation is orthogonal, the wavelength coefficient Pi,j is the image signal and the total power of the noise signal, as shown in the following equation:
(9)VarP=σf2+σn2.Generally, the standard deviationσn of the noise signal is unknown, and the most commonly used method is to estimate the high-frequency subband through the high-wave coefficient after wave decomposition. The standard deviation σn of the noise signal can be obtained by the following formula:
(10)σn=σ^n=medianYi,j0.6745.Among them,Yi,j is a part of the high-frequency subband after wavelet decomposition. Since the scale space is the same, the degree of noise pollution is the same as the wave decomposition coefficient matrix, so for the same subband, only the standard noise distance needs to be estimated. It can be seen from the above two formulas that the energy value of the image signal is
(11)σf2=VarYi,j−σn2.The context model is used to calculate the phase correlation between wave troughs, and the gap is optimized for wave coefficients of different scale groups and spatial directions [8]. At the same time, according to the optimized wavelet threshold, the optimized Wiener filter function is used as the wavelet threshold function, and the Wiener threshold function is used to create an image noise denoising algorithm. Because the improved wave threshold denoising method has a more concentrated threshold function, it can better preserve the edge details and denoising effect.The improved wave threshold denoising function is(12)Pi,j=σf2σf2+σn2Pi,jPi,j≥Thr,0Pi,j<Thr.0


### 3.1.3. Simulation Analysis

In order to verify the effect of the improved wave threshold denoising algorithm, a standard512×512 pixel Lena image was added and denoised, and the algorithm was compared with soft threshold algorithm, hard threshold algorithm, and Bayesian threshold. Research through subjective evaluation and objective evaluation is performed to evaluate the impact of image denoising.First, add Gaussian white noise with a standard deviationσn equal to 10-60 to the Lena standard image, then implement the three-wave decomposition of the orthogonal wave based on the sym4 of the noise image, and apply different denoising algorithms for denoising. The comparison is given in Tables 1 and 2. The calculation formula of the average value of square error MSE and maximum signal-to-noise ratio PSNR is as follows:
(13)MSE=1M×N∑i=1M∑j=1NFi,j−F∧i,j2,PSNR=10×log102552×M×N∑i=1∑j=lFi,j−Fi,j.Table 1
Comparison table of MSE values.
Noise standard deviation102030405060Noisy image99.988389.704825.7561359.3561931.7752494.355Soft threshold algorithm71.28783.00393.422105.869121.733144.137Hard threshold algorithm51.51970.97991.535113.559135.810161.603Bayes threshold algorithm51.61970.36289.478109.703130.500155.339Algorithm22.68844.43263.94685.035108.193134.917Table 2
Peak signal-to-noise ratio (PSNR) value comparison table.
Noise standard deviation102030405060Noisy image28.13122.22318.96216.79715.27114.161Soft threshold algorithm29.60128.94028.42627.88327.27726.543Hard threshold algorithm31.01129.62028.51527.57926.80126.046Bayes threshold algorithm31.00329.65728.61427.72926.97526.218Algorithm34.57331.65430.07328.83527.78926.830Among them,Fi,j is the gray value of the original image at point (i,j); it is denoised by M and N. Both of them are valued from the number of rows and columns in the image.It can be seen from two or more tables below the same noise level that the improved method of denoising threshold conforms to the context model, and the effect of Wiener filtering is much better than other comparison algorithms. The peak signal after denoising has been greatly improved. It can also be seen from the table that the mean square method in this paper is also smaller than that in other algorithms. According to objective evaluation criteria, it is determined that the improved method has better results [9–12].The literature proposes a method to denoise contour images based on the context model. The algorithm combines the models under the condition of changing the contour and calculates an appropriate noise reduction threshold according to its distribution characteristics to obtain a good noise reduction effect. Compare the algorithm in this article with this algorithm, and calculate the peak signal-to-noise ratio under different noise levels. The results are shown in Table3.Table 3
Comparison table of PSNR value between the algorithm in this paper and the algorithm in literature.
Noise standard deviation1015202530Algorithms in the literature31.1529.8529.0527.9927.26Algorithm34.5632.8131.6730.7830.05


## 3.1.1. Embedded Technology

The embedded technology has good prospects for development, and it is a complete computer system. It combines software and hardware and can run independently. At the same time, the embedded system is also a kind of “device” program; it can be integrated into a specific embedded environment, through assembly and configuration of “smart” controls to control specific facilities and equipment, in order to achieve the purpose of “smart” [6]. The device integrated with the built-in system has better portability and speed.This makes it able to capture more target functions and improves the embedded system. These advantages make it have more and more extensive application fields.Embedded system is a computer system that combines software and hardware and can run independently. It is mainly composed of a built-in operating system, peripheral access devices, a built-in processor, and advanced user software. Hardware and software are the two basic components of the built-in system. The hardware tools lay the foundation of the platform for executing the software functions of the embedded system; the built-in operating system and related implementation software are the core of the entire system, controlling and providing appropriate human-computer interaction services.


## 3.1.2. Image Processing Model

In MXN, any image can be regarded as a two-dimensional matrix. After transforming the wavelet, the image will be divided into 4 parts of equal size. If you want to expand the second corrugated layer, you must select the low-frequency component (CA1) obtained after the corrugated layer to obtain the short frequency and the second layer.An example of a noise-contaminated image model is(1)pi,j=fi,j+ni,j.After wavelet decomposition, the results are as follows:(2)Wpi,j=Wfi,j+Wni,j.It can be simplified as follows:(3)Pi,j=Fi,j+Ni,j.After obtaining the wave threshold after the image-modified wave coefficient is obtained, it is necessary to select an appropriate threshold and develop a reasonable threshold function to process the wave coefficient to obtain an estimated value,Fi,jtoFˆi,j, where the threshold value divides the wave coefficient into different parts, and the different parts are set to zero, which are maintained or shortened by the threshold function. Therefore, the selection of the threshold and the movement of the threshold are important parts of the defluctuation threshold.Commonly used threshold functions are divided into hard threshold functions and soft threshold functions.The hard threshold function is(4)Pi,j=Pi,jPi,j≥Thr,0Pi,j<Thr.The soft threshold function is(5)Pi,j=sgnPi,jPi,j−ThrPi,j≥Thr,0Pi,j<Thr.The Wiener filter is(6)Pi,j=σf2σf2+σn2Pi,j.Zi,j can be calculated by the following formula:
(7)Zi,j=19∑k,le0,±/Yi−k,j−l.Among them,Yi,j is the wavelet subband coefficient after wavelet attenuation; the value of k,j is 0 and plus or minus 1; that is, the wavelet coefficients of nine points around the point (i,j) are calculated to obtain the context value.After obtaining the context value, the intrinsic relationship between the context value and the fluctuation coefficient must also be found [7]. We have performed two-layer and three-layer wave decomposition in the noisy Lena image and obtained the horizontal wavelet coefficients after the two-layer and three-layer wave decomposition and calculated the Zi,j context value of each position view. Using the context value Zi,j and the fluctuation coefficient Yi,j as the x-coordinate and y-coordinate, draw an image with the relationship between the context value and the fluctuation coefficient in pairs and use the relationship diagram shown.In wavelet denoising, we always use Bayesian threshold as the wavelet denoising threshold. The Bayesian threshold is calculated by the following formula:(8)Thr=σn2σf.It can be seen from the formula that the calculation of the Bayesian threshold first needs to effectively estimate the value of the signal energyσf2 and the standard value of the noise deviation σn2 to obtain the wave coefficient Pi,j, which is a typical signal and image noise mixing matrix of waveforms. The noise is considered to be an independent and given Gaussian white noise that satisfies 2ni,j~N0,σn, and since the wavelet attenuation is orthogonal, the wavelength coefficient Pi,j is the image signal and the total power of the noise signal, as shown in the following equation:
(9)VarP=σf2+σn2.Generally, the standard deviationσn of the noise signal is unknown, and the most commonly used method is to estimate the high-frequency subband through the high-wave coefficient after wave decomposition. The standard deviation σn of the noise signal can be obtained by the following formula:
(10)σn=σ^n=medianYi,j0.6745.Among them,Yi,j is a part of the high-frequency subband after wavelet decomposition. Since the scale space is the same, the degree of noise pollution is the same as the wave decomposition coefficient matrix, so for the same subband, only the standard noise distance needs to be estimated. It can be seen from the above two formulas that the energy value of the image signal is
(11)σf2=VarYi,j−σn2.The context model is used to calculate the phase correlation between wave troughs, and the gap is optimized for wave coefficients of different scale groups and spatial directions [8]. At the same time, according to the optimized wavelet threshold, the optimized Wiener filter function is used as the wavelet threshold function, and the Wiener threshold function is used to create an image noise denoising algorithm. Because the improved wave threshold denoising method has a more concentrated threshold function, it can better preserve the edge details and denoising effect.The improved wave threshold denoising function is(12)Pi,j=σf2σf2+σn2Pi,jPi,j≥Thr,0Pi,j<Thr.0


## 3.1.3. Simulation Analysis

In order to verify the effect of the improved wave threshold denoising algorithm, a standard512×512 pixel Lena image was added and denoised, and the algorithm was compared with soft threshold algorithm, hard threshold algorithm, and Bayesian threshold. Research through subjective evaluation and objective evaluation is performed to evaluate the impact of image denoising.First, add Gaussian white noise with a standard deviationσn equal to 10-60 to the Lena standard image, then implement the three-wave decomposition of the orthogonal wave based on the sym4 of the noise image, and apply different denoising algorithms for denoising. The comparison is given in Tables 1 and 2. The calculation formula of the average value of square error MSE and maximum signal-to-noise ratio PSNR is as follows:
(13)MSE=1M×N∑i=1M∑j=1NFi,j−F∧i,j2,PSNR=10×log102552×M×N∑i=1∑j=lFi,j−Fi,j.Table 1
Comparison table of MSE values.
Noise standard deviation102030405060Noisy image99.988389.704825.7561359.3561931.7752494.355Soft threshold algorithm71.28783.00393.422105.869121.733144.137Hard threshold algorithm51.51970.97991.535113.559135.810161.603Bayes threshold algorithm51.61970.36289.478109.703130.500155.339Algorithm22.68844.43263.94685.035108.193134.917Table 2
Peak signal-to-noise ratio (PSNR) value comparison table.
Noise standard deviation102030405060Noisy image28.13122.22318.96216.79715.27114.161Soft threshold algorithm29.60128.94028.42627.88327.27726.543Hard threshold algorithm31.01129.62028.51527.57926.80126.046Bayes threshold algorithm31.00329.65728.61427.72926.97526.218Algorithm34.57331.65430.07328.83527.78926.830Among them,Fi,j is the gray value of the original image at point (i,j); it is denoised by M and N. Both of them are valued from the number of rows and columns in the image.It can be seen from two or more tables below the same noise level that the improved method of denoising threshold conforms to the context model, and the effect of Wiener filtering is much better than other comparison algorithms. The peak signal after denoising has been greatly improved. It can also be seen from the table that the mean square method in this paper is also smaller than that in other algorithms. According to objective evaluation criteria, it is determined that the improved method has better results [9–12].The literature proposes a method to denoise contour images based on the context model. The algorithm combines the models under the condition of changing the contour and calculates an appropriate noise reduction threshold according to its distribution characteristics to obtain a good noise reduction effect. Compare the algorithm in this article with this algorithm, and calculate the peak signal-to-noise ratio under different noise levels. The results are shown in Table3.Table 3
Comparison table of PSNR value between the algorithm in this paper and the algorithm in literature.
Noise standard deviation1015202530Algorithms in the literature31.1529.8529.0527.9927.26Algorithm34.5632.8131.6730.7830.05


## 3.2. VR Environment Design Model

The so-called image recording is to find the conversion relationship between several images. The images must meet the same area, especially the same scene, and then match the images. The success of image merging, the success of the algorithm, and the speed of the algorithm directly depend on the image recording, so the image recording may be the most important part of the entire image acquisition process.Mathematically, an image is defined as follows: the reference image and the image to be recorded represent each other, and the registration relationship of the following two images is as follows:(14)I2x,y=gI1fx,y.In the mathematical explanation, it can be seen from the principle of image recording that the more accurate the image recording, the better the conversion between the two images. The change here refers to the relationship between the change of the position and the change of the person, and the record of the final image is known. It is usually not necessary to know its value, so the task of recording the image is to make the most of it. Therefore, equation (14) can be simplified by equation (15).
(15)I2x,y=I1fx,y.The change is a strict change to the rigid body. Rigid body modification models are usually used to record images containing rigid objects such as cars and buildings. It can be expressed by the following formula:(16)x′y′=cosθsinθ−sinθcosθxy+txty.If the object in the combined image not only shows movement, translation, and rotation but also has zooming movement, that is, the size of the object changes, but the shape does not change, then this is a change or a similar change. The uniformity change is expressed by the following formula:(17)x′y′=scosθsinθ−sinθcosθxy+txty.This article uses this pattern to change the recording and stitching of images. Before and after conversion, the line position of the image remains unchanged, and the rest can be changed, such as the shape and size. Therefore, this conversion can satisfy most of the images that are usually collected. This article will use this conversion and behave as the following formula:(18)x′y′=a1a2a3a4xy+txty.In this change, unlike the affine change mode, the linear position relationship before and after the change changes from the initial alignment of the intersection point, and the position change between the spatial images or the movement of the zoom ratio can be expressed as(19)x′y′z′=m0m1m2m3m4m5m6m7m8xyw.The parameters of the formula are expressed bymi, and mii is the 8 degrees of freedom of the matrix, expressed from m0 to m7.
(20)x′=m0x+m1y+m2m6x+m7y+m8,y′=m3x+m4y+m5m6x+m7y+m8.The recording method mainly includes two different methods: methods based on Fourier transform and wavelet transform. In the Fourier transform, the position of the image is changed and is not disturbed by external noise. However, the application of this change in the field of image recording also has disadvantages. For example, it can only record partial images, and the attributes of these images are grayscale with associated wheels. In the domain transform, the Fourier transform method is the most classic.These two images are the reference image and the target image. We assume that the result of the change isG1u,v and G2u,v and obtain the following formula according to the relevant Fourier change:
(21)Φu,v=G1u,vG2∗u,v.The correlation function of the spatial domain obtained by the inverse Fourier transform of the domain formula is as follows:(22)Φu,v=∑u=0M−1∑u=0M−1G1u,vG2∗u,vωM−ujωM−vj.The direct mediation method uses the pixel gray value obtained from the coverage area of the recorded image, and the steps are as follows: find the average value and use the average value to merge the images. The algorithm will filter low-pass images, and the security images will appear banded, which directly leads to a decrease in the quality of the combined images. The method is expressed as follows; the two combined images are denoted byf and f, respectively; and the fused image is denoted by f:
(23)fx,y=f1x,yx,y∈f1,f1x,y+f2x,y2x,y∈f1∩f2,f2x,yx,y∈f2.Combining multiple images involves the same principle. At this time, the overlapping area can be expressed as(24)fx,y=1n∑ifix,y,wherex,y∈f1∩f2∩⋯fn.The last step of the algorithm is to obtain the corner point and determine the position of the corner point by calculating the maximum local value (that is, the response of the first corner point) [13].The algorithm has a faster calculation speed, is more accurate when finding corners, and is not interfered by noise. Therefore, researchers in many fields have carefully studied the algorithm, but it is difficult to know the standardt and g when setting a specific value, and it is difficult to detect edge corners.


## 4. Digital Museum VR Scene Modeling Design and Application

### 4.1. Digital Museum Scene Modeling

Hybrid modeling technology combines the advantages and disadvantages of two different modeling technologies to model the real scene to meet the user’s observation needs, collects image information from multiple angles, and accesses the data through computer processing to create a more realistic model visual experience, but for interactive modules and when using modeling scenes, geometry-based methods are used for modeling [14].Although the hybrid modeling method has many advantages, the hybrid modeling method also brings us many modeling difficulties used in the scene, for example, how to fit the two-dimensional objects obtained after processing [15]. In the image information of the virtual entity coordination in the virtual world, in addition, how to switch between different perspectives is all issues that should be considered in the hybrid modeling method. Table 4 shows the comparison results of three virtual landscape modeling methods.Table 4
Comparison of three modeling methods.
Virtual modeling methodAdvantageDisadvantageModeling method based on geometric renderingThe model is fine and accurate, has strong interaction, can freely control the viewpoint and angle of view, is and observed in multiple directionsLarge amount of calculation, high hardware requirements, difficult to model and express complex scenesModeling method based on image renderingModeling is easy; drawing speed is fast; realism is strong; the amount of calculation is small; and the hardware requirements are lowImage preprocessing time is long, interactivity is poor, and free roaming is difficultHybrid modeling method based on geometry and imageCombining the advantages of the above two methods, strong sense of reality; good interactivityIssues such as seamless scene connection and scene exchange during interaction have yet to be resolved


### 4.2. Virtual Museum Modeling Process

In the virtual museum scene modeling system, this paper adopts a hybrid modeling method to create a more realistic and interactive virtual scene of the museum scene. Combining the geometry-based modeling method and the image source transmission method, multiple photos of the museum scene, photos displayed in the museum culture museum, museum background photos, etc. can be used to virtually model the museum scene [16–19]. Finally, use geometry-based modeling methods to construct landscape objects, and process the model through screenshots of cultural relics in different directions. Finally, a virtual museum scene was obtained through the hybrid method, which contained many objects, different angles, and details of the realistic scenery.


#### 4.2.1. Hierarchical Structure of Virtual Scene

Due to the wide variety of cultural relics displayed in the museum itself, we have to model each cultural relic model in detail, which adds a lot of difficulties to our modeling. For this reason, this article proposes a reasonable division rule to divide the exhibition hall of the museum to avoid wasting resources and repetitive modeling [20]. This article proposes to divide different types of cultural relics into different exhibition halls according to different times. The themes of cultural relics are shared in different rooms of the exhibition, and the model in this article is constructed from top to bottom according to the structure shown in the figure.The idea presented in the first mock exam not only simplifies and classifies the complexity of the scene but also improves the scene creation in real time. Moreover, the time of the same model is shortened by the reuse of the model, thus improving the efficiency of the overall system modeling [21, 22].


### 4.3. 3D Scene Roaming Technology

#### 4.3.1. Concept of 3D Roaming System

3D exploration system is not only a real space but also a real virtual space simulated in a virtual space, which is a virtual environment with a specific scope. Virtual environment has the characteristics of combining vision, hearing, and touch [23]. Roaming users are naturally in the virtual space, and users must use the necessary equipment in the space. Roaming users can not only see virtual objects from all directions and make them feel in mind but also plan and run internal things.


#### 4.3.2. 3D Scene Management

The main task of stage management is to achieve spatial sorting, index, and process, all of which are aimed at the landscape objects in colleges and universities, and the basis of consciousness is effective data organization structure. Among them, the key of stage management is the form of data organization, technology organization, and spatial simplification computing technology.


#### 4.3.3. Collision Detection Algorithm Based on Bounding Box

The main technology of this method is to evaluate the specific method of two collision objects by calculating the distance and radius difference of the center point. When a simplified object is treated as an element, the subject stem is obtained [24]. The basic idea of this method is to draw a boundary box with the same geometric pattern as the object and then carry out crossexperiments between the boundary boxes to exclude different main elements. However, when participating in collision detection, we will not use such complex geometric objects but use simple shapes (most of the sizes) [25]. There are several identification methods, as shown in the table below.In Table5, as the name suggests, the method for a spherical bounding box is to determine whether an object or a partially projected object represents an intersecting sphere. This is one of the simplest collision detection algorithms, which is used in the double collision proposed in this paper.Table 5
Comparison of five bounding box methods.
Bounding box methodTightnessDetection speedDifficulty of detectionDetection accuracyScope of applicationSpherical bounding boxDifferenceFastEasyDifferenceWidthAABBPoorFastEasierMediumWidthOBBMediumFasterEasierMediumWiderK-DOPBetterFasterMediumBetterNarrowerFDHIt is goodSlowHarderBetterNarrower


### 4.4. Realization of Digital Museum Scene

The three-dimensional dynamic landscape means moving the images built into the dynamic panorama to the space to achieve the purpose of browsing different scenes and making users feel deep. Make three-dimensional dynamic scenes (both simple and complex) according to virtual reality modeling (VRML language) and Java to control VRML viewing.


#### 4.4.1. Realization of Simple 3D Dynamic Scene Technology

(1) Introduction to VRML (Virtual Reality Modeling Language). The VRML language is a three-dimensional language model, a translated, Web-oriented, and object-oriented language. A complex scene consists of a collection of child nodes, where the nodes are objects in VRML. Nodes, events, scenes, prototypes, scripts, and paths constitute a complete VRML file. In this way, we can not only create a reasonable three-dimensional virtual scene but also enable users to communicate well with the virtual scene [26]. On the Internet, the state of two-dimensional photography has changed. All of this is done through VRML files. And they can use interpolators and sensors; these devices have built-in VRML and finally achieve the effect of three-dimensional roaming.At present, VRML has the advantages of platform independence, rapid scalability, good ability to run under low bandwidth, and strong interactivity in 3D scene recognition. This technology has leading advantages in many fields, and a lot of applied research has been carried out.(2) VRML Realizes Simple Dynamic Scene. VRML itself has multiple nodes, including time sensor node, interpolator node, and detector node, which are numbered 1, 6, and 7, respectively. It is usually used to combine three different nodes, and the animation effect is more vivid and lifelike. If used properly, it can make users feel deeply [27]. Assign important animation time points to the time sensor node, and complete the animation description process at the interpolator node, so that the detector node can detect user behavior and detect user position. However, the use of nodes can only be completed in simple three-dimensional animations, and for complex animations, it is more cumbersome or even impossible to achieve [28].


#### 4.4.2. Realization of Complex 3D Dynamic Scene Technology

(1) VRML and Java Technology. VRML is platform-independent. If you know the three-dimensional scene information, you can easily extend it. This is a key virtual reality technology. The VRML browser has many functions. For example, it not only makes the interface more realistic and easy for users to navigate but also has a lot of information and can communicate well [29]. Sometimes, it is a standalone program, and sometimes, it is a link and a Web page plug-in. Therefore, it is more realistic to know how to perform online simulations (such as online teaching, battlefield simulation, and online practice) [30] in any actual scenario. It is widely used in practice.Java is a network programming language. The basic functions and structure are very similar to C++, but the Java language is much simpler than C++. The Java language has many functions, and the software developed in this language has strong practicability, good audiovisual effects, and fast operation.(2) Reasons for the Combination of VRML Technology and Java. VRML technology and Java language are different. VRML is a three-dimensional language model and language translation. It is Web-oriented and object-oriented and is the core technology for the development and realization of virtual reality. In this way, we can not only create realistic three-dimensional virtual scenes but also enable users to interact well with the virtual scenes. However, this interaction is not very effective in the perception process and is static [31]. In this way, because VRML uses the main elements of this component, it is difficult to implement other functions of the VRML system. Therefore, in order to make up for this gap, we use Java scripting language to control the VRML environment to make it the most effective development and expansion of the behavior of the virtual environment [32].Java is a very effective tool, especially for Web application development. Java applications are embedded in the Web. Java applet is based on Java language, combined with VRML technology, and was later brought to the network, which can better realize virtual roaming interaction in dynamic scenes.


### 4.5. Digital Museum Scene Display

#### 4.5.1. Introduction to Canvas Elements

One of the key components of HTML5 is the Canvas element. This element is easy to use, drawing the Canvas element is done in JavaScript, and JavaScript cannot draw by itself. JavaScript is the scripting language of the browser, and it is also a crossplatform language. In order to produce dynamic effects on the Web, we merge or migrate the JavaScript language to HTML to complete the operation [33]. JavaScript is only related to the browser; that is, as long as the user’s browser supports scripting languages, it can run normally regardless of the platform.There are many similarities between Canvas and Flash in HTML5. Both of them can draw pictures and play animations, and each activity has its own advantages and disadvantages [34]. So far, Flash applications have matured and been widely used. Flash efficiency for drawing graphics and bitmaps is much faster than HTML5 Canvas; HTML5 Canvas features are better than Flash. There is no need to install other plug-ins during playback, and it has good consistency, which makes it easier to integrate and interact with other elements of the Web page.


#### 4.5.2. Virtual Scene Display

Users can visit the museum’s homepage, select their favorite virtual scenes, and then browse them, view cultural relic exhibitions without leaving home, and complete a series of operations. The above conditions are all completed in the Canvas HTML5 browser.However, simple navigation in the museum is far from satisfying the needs of users. In order to ensure the authenticity of the scene and maintain a good interaction between users and cultural relics, we need to download specific plug-ins when we roam in the virtual museum. Therefore, in order to complete the scene presentation, not only the scene files need to be analyzed but also the drawing module must be called using the Canvas element. These scene data files are retrieved by the browser through the server.


## 4.1. Digital Museum Scene Modeling

Hybrid modeling technology combines the advantages and disadvantages of two different modeling technologies to model the real scene to meet the user’s observation needs, collects image information from multiple angles, and accesses the data through computer processing to create a more realistic model visual experience, but for interactive modules and when using modeling scenes, geometry-based methods are used for modeling [14].Although the hybrid modeling method has many advantages, the hybrid modeling method also brings us many modeling difficulties used in the scene, for example, how to fit the two-dimensional objects obtained after processing [15]. In the image information of the virtual entity coordination in the virtual world, in addition, how to switch between different perspectives is all issues that should be considered in the hybrid modeling method. Table 4 shows the comparison results of three virtual landscape modeling methods.Table 4
Comparison of three modeling methods.
Virtual modeling methodAdvantageDisadvantageModeling method based on geometric renderingThe model is fine and accurate, has strong interaction, can freely control the viewpoint and angle of view, is and observed in multiple directionsLarge amount of calculation, high hardware requirements, difficult to model and express complex scenesModeling method based on image renderingModeling is easy; drawing speed is fast; realism is strong; the amount of calculation is small; and the hardware requirements are lowImage preprocessing time is long, interactivity is poor, and free roaming is difficultHybrid modeling method based on geometry and imageCombining the advantages of the above two methods, strong sense of reality; good interactivityIssues such as seamless scene connection and scene exchange during interaction have yet to be resolved


## 4.2. Virtual Museum Modeling Process

In the virtual museum scene modeling system, this paper adopts a hybrid modeling method to create a more realistic and interactive virtual scene of the museum scene. Combining the geometry-based modeling method and the image source transmission method, multiple photos of the museum scene, photos displayed in the museum culture museum, museum background photos, etc. can be used to virtually model the museum scene [16–19]. Finally, use geometry-based modeling methods to construct landscape objects, and process the model through screenshots of cultural relics in different directions. Finally, a virtual museum scene was obtained through the hybrid method, which contained many objects, different angles, and details of the realistic scenery.


### 4.2.1. Hierarchical Structure of Virtual Scene

Due to the wide variety of cultural relics displayed in the museum itself, we have to model each cultural relic model in detail, which adds a lot of difficulties to our modeling. For this reason, this article proposes a reasonable division rule to divide the exhibition hall of the museum to avoid wasting resources and repetitive modeling [20]. This article proposes to divide different types of cultural relics into different exhibition halls according to different times. The themes of cultural relics are shared in different rooms of the exhibition, and the model in this article is constructed from top to bottom according to the structure shown in the figure.The idea presented in the first mock exam not only simplifies and classifies the complexity of the scene but also improves the scene creation in real time. Moreover, the time of the same model is shortened by the reuse of the model, thus improving the efficiency of the overall system modeling [21, 22].


## 4.2.1. Hierarchical Structure of Virtual Scene

Due to the wide variety of cultural relics displayed in the museum itself, we have to model each cultural relic model in detail, which adds a lot of difficulties to our modeling. For this reason, this article proposes a reasonable division rule to divide the exhibition hall of the museum to avoid wasting resources and repetitive modeling [20]. This article proposes to divide different types of cultural relics into different exhibition halls according to different times. The themes of cultural relics are shared in different rooms of the exhibition, and the model in this article is constructed from top to bottom according to the structure shown in the figure.The idea presented in the first mock exam not only simplifies and classifies the complexity of the scene but also improves the scene creation in real time. Moreover, the time of the same model is shortened by the reuse of the model, thus improving the efficiency of the overall system modeling [21, 22].


## 4.3. 3D Scene Roaming Technology

### 4.3.1. Concept of 3D Roaming System

3D exploration system is not only a real space but also a real virtual space simulated in a virtual space, which is a virtual environment with a specific scope. Virtual environment has the characteristics of combining vision, hearing, and touch [23]. Roaming users are naturally in the virtual space, and users must use the necessary equipment in the space. Roaming users can not only see virtual objects from all directions and make them feel in mind but also plan and run internal things.


### 4.3.2. 3D Scene Management

The main task of stage management is to achieve spatial sorting, index, and process, all of which are aimed at the landscape objects in colleges and universities, and the basis of consciousness is effective data organization structure. Among them, the key of stage management is the form of data organization, technology organization, and spatial simplification computing technology.


### 4.3.3. Collision Detection Algorithm Based on Bounding Box

The main technology of this method is to evaluate the specific method of two collision objects by calculating the distance and radius difference of the center point. When a simplified object is treated as an element, the subject stem is obtained [24]. The basic idea of this method is to draw a boundary box with the same geometric pattern as the object and then carry out crossexperiments between the boundary boxes to exclude different main elements. However, when participating in collision detection, we will not use such complex geometric objects but use simple shapes (most of the sizes) [25]. There are several identification methods, as shown in the table below.In Table5, as the name suggests, the method for a spherical bounding box is to determine whether an object or a partially projected object represents an intersecting sphere. This is one of the simplest collision detection algorithms, which is used in the double collision proposed in this paper.Table 5
Comparison of five bounding box methods.
Bounding box methodTightnessDetection speedDifficulty of detectionDetection accuracyScope of applicationSpherical bounding boxDifferenceFastEasyDifferenceWidthAABBPoorFastEasierMediumWidthOBBMediumFasterEasierMediumWiderK-DOPBetterFasterMediumBetterNarrowerFDHIt is goodSlowHarderBetterNarrower


## 4.3.1. Concept of 3D Roaming System

3D exploration system is not only a real space but also a real virtual space simulated in a virtual space, which is a virtual environment with a specific scope. Virtual environment has the characteristics of combining vision, hearing, and touch [23]. Roaming users are naturally in the virtual space, and users must use the necessary equipment in the space. Roaming users can not only see virtual objects from all directions and make them feel in mind but also plan and run internal things.


## 4.3.2. 3D Scene Management

The main task of stage management is to achieve spatial sorting, index, and process, all of which are aimed at the landscape objects in colleges and universities, and the basis of consciousness is effective data organization structure. Among them, the key of stage management is the form of data organization, technology organization, and spatial simplification computing technology.


## 4.3.3. Collision Detection Algorithm Based on Bounding Box

The main technology of this method is to evaluate the specific method of two collision objects by calculating the distance and radius difference of the center point. When a simplified object is treated as an element, the subject stem is obtained [24]. The basic idea of this method is to draw a boundary box with the same geometric pattern as the object and then carry out crossexperiments between the boundary boxes to exclude different main elements. However, when participating in collision detection, we will not use such complex geometric objects but use simple shapes (most of the sizes) [25]. There are several identification methods, as shown in the table below.In Table5, as the name suggests, the method for a spherical bounding box is to determine whether an object or a partially projected object represents an intersecting sphere. This is one of the simplest collision detection algorithms, which is used in the double collision proposed in this paper.Table 5
Comparison of five bounding box methods.
Bounding box methodTightnessDetection speedDifficulty of detectionDetection accuracyScope of applicationSpherical bounding boxDifferenceFastEasyDifferenceWidthAABBPoorFastEasierMediumWidthOBBMediumFasterEasierMediumWiderK-DOPBetterFasterMediumBetterNarrowerFDHIt is goodSlowHarderBetterNarrower


## 4.4. Realization of Digital Museum Scene

The three-dimensional dynamic landscape means moving the images built into the dynamic panorama to the space to achieve the purpose of browsing different scenes and making users feel deep. Make three-dimensional dynamic scenes (both simple and complex) according to virtual reality modeling (VRML language) and Java to control VRML viewing.


### 4.4.1. Realization of Simple 3D Dynamic Scene Technology

(1) Introduction to VRML (Virtual Reality Modeling Language). The VRML language is a three-dimensional language model, a translated, Web-oriented, and object-oriented language. A complex scene consists of a collection of child nodes, where the nodes are objects in VRML. Nodes, events, scenes, prototypes, scripts, and paths constitute a complete VRML file. In this way, we can not only create a reasonable three-dimensional virtual scene but also enable users to communicate well with the virtual scene [26]. On the Internet, the state of two-dimensional photography has changed. All of this is done through VRML files. And they can use interpolators and sensors; these devices have built-in VRML and finally achieve the effect of three-dimensional roaming.At present, VRML has the advantages of platform independence, rapid scalability, good ability to run under low bandwidth, and strong interactivity in 3D scene recognition. This technology has leading advantages in many fields, and a lot of applied research has been carried out.(2) VRML Realizes Simple Dynamic Scene. VRML itself has multiple nodes, including time sensor node, interpolator node, and detector node, which are numbered 1, 6, and 7, respectively. It is usually used to combine three different nodes, and the animation effect is more vivid and lifelike. If used properly, it can make users feel deeply [27]. Assign important animation time points to the time sensor node, and complete the animation description process at the interpolator node, so that the detector node can detect user behavior and detect user position. However, the use of nodes can only be completed in simple three-dimensional animations, and for complex animations, it is more cumbersome or even impossible to achieve [28].


### 4.4.2. Realization of Complex 3D Dynamic Scene Technology

(1) VRML and Java Technology. VRML is platform-independent. If you know the three-dimensional scene information, you can easily extend it. This is a key virtual reality technology. The VRML browser has many functions. For example, it not only makes the interface more realistic and easy for users to navigate but also has a lot of information and can communicate well [29]. Sometimes, it is a standalone program, and sometimes, it is a link and a Web page plug-in. Therefore, it is more realistic to know how to perform online simulations (such as online teaching, battlefield simulation, and online practice) [30] in any actual scenario. It is widely used in practice.Java is a network programming language. The basic functions and structure are very similar to C++, but the Java language is much simpler than C++. The Java language has many functions, and the software developed in this language has strong practicability, good audiovisual effects, and fast operation.(2) Reasons for the Combination of VRML Technology and Java. VRML technology and Java language are different. VRML is a three-dimensional language model and language translation. It is Web-oriented and object-oriented and is the core technology for the development and realization of virtual reality. In this way, we can not only create realistic three-dimensional virtual scenes but also enable users to interact well with the virtual scenes. However, this interaction is not very effective in the perception process and is static [31]. In this way, because VRML uses the main elements of this component, it is difficult to implement other functions of the VRML system. Therefore, in order to make up for this gap, we use Java scripting language to control the VRML environment to make it the most effective development and expansion of the behavior of the virtual environment [32].Java is a very effective tool, especially for Web application development. Java applications are embedded in the Web. Java applet is based on Java language, combined with VRML technology, and was later brought to the network, which can better realize virtual roaming interaction in dynamic scenes.


## 4.4.1. Realization of Simple 3D Dynamic Scene Technology

(1) Introduction to VRML (Virtual Reality Modeling Language). The VRML language is a three-dimensional language model, a translated, Web-oriented, and object-oriented language. A complex scene consists of a collection of child nodes, where the nodes are objects in VRML. Nodes, events, scenes, prototypes, scripts, and paths constitute a complete VRML file. In this way, we can not only create a reasonable three-dimensional virtual scene but also enable users to communicate well with the virtual scene [26]. On the Internet, the state of two-dimensional photography has changed. All of this is done through VRML files. And they can use interpolators and sensors; these devices have built-in VRML and finally achieve the effect of three-dimensional roaming.At present, VRML has the advantages of platform independence, rapid scalability, good ability to run under low bandwidth, and strong interactivity in 3D scene recognition. This technology has leading advantages in many fields, and a lot of applied research has been carried out.(2) VRML Realizes Simple Dynamic Scene. VRML itself has multiple nodes, including time sensor node, interpolator node, and detector node, which are numbered 1, 6, and 7, respectively. It is usually used to combine three different nodes, and the animation effect is more vivid and lifelike. If used properly, it can make users feel deeply [27]. Assign important animation time points to the time sensor node, and complete the animation description process at the interpolator node, so that the detector node can detect user behavior and detect user position. However, the use of nodes can only be completed in simple three-dimensional animations, and for complex animations, it is more cumbersome or even impossible to achieve [28].


## 4.4.2. Realization of Complex 3D Dynamic Scene Technology

(1) VRML and Java Technology. VRML is platform-independent. If you know the three-dimensional scene information, you can easily extend it. This is a key virtual reality technology. The VRML browser has many functions. For example, it not only makes the interface more realistic and easy for users to navigate but also has a lot of information and can communicate well [29]. Sometimes, it is a standalone program, and sometimes, it is a link and a Web page plug-in. Therefore, it is more realistic to know how to perform online simulations (such as online teaching, battlefield simulation, and online practice) [30] in any actual scenario. It is widely used in practice.Java is a network programming language. The basic functions and structure are very similar to C++, but the Java language is much simpler than C++. The Java language has many functions, and the software developed in this language has strong practicability, good audiovisual effects, and fast operation.(2) Reasons for the Combination of VRML Technology and Java. VRML technology and Java language are different. VRML is a three-dimensional language model and language translation. It is Web-oriented and object-oriented and is the core technology for the development and realization of virtual reality. In this way, we can not only create realistic three-dimensional virtual scenes but also enable users to interact well with the virtual scenes. However, this interaction is not very effective in the perception process and is static [31]. In this way, because VRML uses the main elements of this component, it is difficult to implement other functions of the VRML system. Therefore, in order to make up for this gap, we use Java scripting language to control the VRML environment to make it the most effective development and expansion of the behavior of the virtual environment [32].Java is a very effective tool, especially for Web application development. Java applications are embedded in the Web. Java applet is based on Java language, combined with VRML technology, and was later brought to the network, which can better realize virtual roaming interaction in dynamic scenes.


## 4.5. Digital Museum Scene Display

### 4.5.1. Introduction to Canvas Elements

One of the key components of HTML5 is the Canvas element. This element is easy to use, drawing the Canvas element is done in JavaScript, and JavaScript cannot draw by itself. JavaScript is the scripting language of the browser, and it is also a crossplatform language. In order to produce dynamic effects on the Web, we merge or migrate the JavaScript language to HTML to complete the operation [33]. JavaScript is only related to the browser; that is, as long as the user’s browser supports scripting languages, it can run normally regardless of the platform.There are many similarities between Canvas and Flash in HTML5. Both of them can draw pictures and play animations, and each activity has its own advantages and disadvantages [34]. So far, Flash applications have matured and been widely used. Flash efficiency for drawing graphics and bitmaps is much faster than HTML5 Canvas; HTML5 Canvas features are better than Flash. There is no need to install other plug-ins during playback, and it has good consistency, which makes it easier to integrate and interact with other elements of the Web page.


### 4.5.2. Virtual Scene Display

Users can visit the museum’s homepage, select their favorite virtual scenes, and then browse them, view cultural relic exhibitions without leaving home, and complete a series of operations. The above conditions are all completed in the Canvas HTML5 browser.However, simple navigation in the museum is far from satisfying the needs of users. In order to ensure the authenticity of the scene and maintain a good interaction between users and cultural relics, we need to download specific plug-ins when we roam in the virtual museum. Therefore, in order to complete the scene presentation, not only the scene files need to be analyzed but also the drawing module must be called using the Canvas element. These scene data files are retrieved by the browser through the server.


## 4.5.1. Introduction to Canvas Elements

One of the key components of HTML5 is the Canvas element. This element is easy to use, drawing the Canvas element is done in JavaScript, and JavaScript cannot draw by itself. JavaScript is the scripting language of the browser, and it is also a crossplatform language. In order to produce dynamic effects on the Web, we merge or migrate the JavaScript language to HTML to complete the operation [33]. JavaScript is only related to the browser; that is, as long as the user’s browser supports scripting languages, it can run normally regardless of the platform.There are many similarities between Canvas and Flash in HTML5. Both of them can draw pictures and play animations, and each activity has its own advantages and disadvantages [34]. So far, Flash applications have matured and been widely used. Flash efficiency for drawing graphics and bitmaps is much faster than HTML5 Canvas; HTML5 Canvas features are better than Flash. There is no need to install other plug-ins during playback, and it has good consistency, which makes it easier to integrate and interact with other elements of the Web page.


## 4.5.2. Virtual Scene Display

Users can visit the museum’s homepage, select their favorite virtual scenes, and then browse them, view cultural relic exhibitions without leaving home, and complete a series of operations. The above conditions are all completed in the Canvas HTML5 browser.However, simple navigation in the museum is far from satisfying the needs of users. In order to ensure the authenticity of the scene and maintain a good interaction between users and cultural relics, we need to download specific plug-ins when we roam in the virtual museum. Therefore, in order to complete the scene presentation, not only the scene files need to be analyzed but also the drawing module must be called using the Canvas element. These scene data files are retrieved by the browser through the server.


## 5. Conclusion

This article introduces the latest development of embedded Linux technology and digital image processing technology, integrates the internal Linux system with the digital image processing system for research, and develops a specific universal Linux embedded image processing method. By building an already embedded platform, the miniaturization and real-time creation of image processing platforms, as well as image capture and image processing processes, can be completed on the embedded Linux image processing platform. This paper proposes an improved image-based recording method. In terms of image stitching, in order to achieve a natural and smooth transition of the composite image, the stitching traces have been removed and finally made into a seamless stitching. This article uses an improved algorithm based on the fade-in and fade-out functions. This algorithm realizes the optimal configuration of elements through the ratio of various related elements. In subsequent articles, 3D modeling and landscape technology in virtual museums will be discussed and studied. The surrounding scenes and cultural exhibitions are modeled differently, and virtual modeling of the museum scene will be provided. In particular, the modeling process of cultural relic exhibits focuses on exploration and identification, regarding image recording.



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*Source: 7888791-2022-04-08.xml*