# Design and Reconstruction of Visual Art Based on Virtual Reality

Authors: Bai Yun
Journal: Security and Communication Networks
 (2021)

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## Abstract

Because traditional methods generally lack the image preprocessing link, the effect of visual image detail processing is not good. In order to enhance the image visual effect, a visual art design method based on virtual reality is proposed. Wavelet transform method is used to denoise the visual image and the noise signal in the image is removed; a binary model of fuzzy space vision fusion is established, the space of the visual image is planned, and the spatial distribution information of the visual image is obtained. According to the principle of light and shadow phenomenon in visual image rendering, the Extend Shadow map algorithm is used to render the visual image. Virtual reality technology was used to reconstruct the preprocessed visual image, and the ant colony algorithm was used to optimize the visual image to realize the visual image design. The results show that the peak signal-to-noise ratio of the visual image processed by the proposed method is high, and the image detail processing effect is better.


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## Body

## 1. Introduction

As a communicator of culture in the information age-visual design art [1], it has become more and more deeply involved in people’s daily life and has attracted the attention of the public. As the material manifestation and important carrier of visual design, images are increasingly exerting a strong influence on public life [2]. Although the technical potential in the field of the visual art design is huge and the application prospects are also very broad, there are still many unresolved theoretical problems and unsurpassed technical obstacles that require us to actively explore and pursue.As a category with strong practicality, visual art design still has a large number of directions worthy of deep exploration by art creators. By browsing the literature, we can find that there have been much research studies on visual art design. However, in terms of visual image design, research in this field of art is still relatively fragmented and only stays at a relatively shallow level of case analysis of works and popularization of visual art concepts. Most of them are based on theoretical research, and there are few visual arts. The characteristics and techniques of images, how to systematically use virtual reality as a medium for visual art design research, and the actual use of virtual reality technology for visual art design creation research are even rarer [3]. Reference [4] proposed an image optimization design method based on the theory of visual cognition. First, the morphological analysis method is used to extract the morphological design elements of the product. Secondly, based on the eye-tracking experiment to obtain the eye movement cognition data of the visual form, then, the single factor analysis method is used to screen the eye movement indicators that are highly correlated with the form. Finally, the quantitative type I theory is used to parametrically encode design elements, and the relationship model between eye-tracking indicators and design elements is established through data mining technology. As a result, the relationship model between eye-tracking indicators and design elements is finally obtained, and then the form design elements that are closely related to the user’s visual cognition and their weight rankings are extracted so as to determine the optimal scheme of form design. Take the electric kettle as an example to verify the feasibility of this method. The analysis results show that this method can help designers optimize the product form from the perspective of users’ visual cognition, thereby enhancing the scientificity and rationality of the design. Reference [5] aims to build a bridge between human and computer information and proposes a visual optimization design method based on computer interaction technology. Through a graphical user interface, user demand analysis, data processing, interface image enhancement, interface text optimization, and other steps, the visual optimization design of web interface text is realized. Among them, the data processing adopts big data integration based on computer interaction technology to process the information generated in the interface interaction process, which improves the big data fusion and information clustering capabilities during human-computer interface interaction; Using a visual feature-based web interface image color enhancement algorithm, combined with the optimization of text shape features and color, the web interface text visual optimization design is jointly realized.Network system security mainly refers to the security problems existing in the computer and the network itself, which is to guarantee the availability and security of the e-commerce platform. Its contents include computer physics, system, database, network equipment, network services, and other security problems. Virtual reality inevitably involves information security. The problem of information security is information theft, information tampering, information counterfeiting, and information malicious destruction in the process of the transmission of e-commerce information in the network.Although the above method can improve the visual effect to a certain extent, the visual image is not processed well because of the lack of preprocessing of the visual image. Therefore, a visual art design method based on virtual reality is proposed. A visual image is an important form of visual art. This article takes it as the main research object and takes visual art design as the main starting point. Using virtual reality technology to adjust the light and shade of the object, the color and gloss of the image are coordinated, and the visual tension of the visual image is improved. The experimental results show that this method improves the efficiency of visual image creation. It can make designers use more accurate color matching and filling methods to improve the visual expression of the whole visual design work and create a more comfortable visual image effect.Our contribution is threefold:(1)
Because traditional methods generally lack the image preprocessing link, the effect of visual image detail processing is not good. In order to enhance the image visual effect, a visual art design method based on virtual reality is proposed.(2)
The wavelet transform method is used to denoise the visual image, the noise signal in the image is removed, a binary model of fuzzy space vision fusion is established, the space of the visual image is planned, and the spatial distribution information of the visual image is obtained.(3)
The results show that the peak signal-to-noise ratio of the visual image processed by the proposed method is high, and the image detail processing effect is better.


## 2. Visual Image Preprocessing

Before visual image design, first, preprocess the visual image. This link mainly includes three parts: wavelet denoising, visual image spatial distribution information extraction, and visual image rendering.


### 2.1. Visual Image Wavelet Denoising

The wavelet transform method was used to denoise the visual image [6], assuming that the visual image contains the following noise signals:(1)Fn=ωn+αn.Among them,ωn represents the original signal of the visual image and αn represents the noise contained in the image.Discretize the noise signal in the visual image to obtain the discrete signalkn, where n=0,1,…,N, and use the wavelet transform method to sample some of the signals in the discrete signal:(2)Dj=1nX1j−AVGj+X2j−AVGj+,...,+Xnj−AVGj.Among them,Xnj represents the scale function, Dj represents the wavelet decomposition result of the visual image, and AVGj represents the wavelet coefficient. Further, transform the wavelet coefficients to obtain the following:(3)AVGj=1nX1j+X2j+,...,+Xnj.Assuming that the wavelet function isYij, the filter coefficient matrices corresponding to the scaling function Xnj and the wavelet function Yij are Znj and Lij, which are expressed by formulas (4) and (5), respectively:(4)Znj=1z11⋯z1j1z21⋯z2j⋮⋮⋮1zn1⋯znj,(5)Lij=l1i0⋯00l2i⋯⋮⋮⋮00⋯lij.According to formulas (4) and (5), the visual image is denoted as SZnj,Lij. According to the linear nature of wavelet transform, it can be seen that after discretizing SZnj,Lij, the image obtained is still composed of two parts; these are the coefficient Wk corresponding to the original signal ωn and the coefficient Xk corresponding to the noise signal αn.After the wavelet decomposition of the visual image, the normal signal in the image is highly concentrated on the wavelet coefficients with a larger amplitude, while the noise signal is randomly distributed in the transform domain. At this time, the noise in the wavelet coefficients can be further denoised. In reference [7], the specific denoising process is as follows:Step 1: select appropriate wavelet coefficients, decompose the visual image to be denoised, and obtain theo-level visual image hierarchyStep 2: choose an appropriate threshold and quantify different visual image levels [8]Step 3: according to the quantization processing result, the visual images of each layer are subjected to denoising processing to achieve the overall denoising result


### 2.2. Extraction of Visual Image Spatial Distribution Information

Based on the wavelet denoising of the visual image, the spatial planning of the visual image is carried out to obtain the adaptive spatial distribution value of the visual image:(6)Hμ=λN∑i=1Myi2.Among them,yi represents the gradient pattern of the pixel feature points of the visual image in the i direction. According to the visual scene, a binary model of fuzzy spatial vision fusion is established, and the correlation characteristic of the adaptive spatial visual feature matching of the visual image is obtained by using this model, which is expressed by the following formula:(7)Hμi×n=∑i=1ncirij.Among them,ci represents the texture feature of the visual image and rij represents the pixel array of the image.From this, the local spatial structure component of the visual image can be obtained. On this basis, the linear programming method [9] is further used to control the global spatial threshold ϖdi of the visual image:(8)ϖdi=∑i=1n∑j=1ntpi−opj.Among them,tpi represents the distance of the visual point and opj represents the image reference feature point.Perform visual tracking and matching of the histogram of the visual image with the reference feature points, and establish a multilayer segmentation model of the visual image space in the local area of the image:(9)Zi=∑i,j=1Nexp−di,jϖdi.In the visual image area, the Harris corner point detection method is used to mark the corner points of the image, and the spatial visual information fusion result of the visual image is obtained as follows:(10)Eκ=∑i,j=1nφμi,j×Zi.Among them,φμ represents the image texture coordinate space change rate and κ represents the image quantization coding.Use the edge contour feature extraction method to extract the spatial distribution information of the visual image in the fusion result:(11)Wφ=1I1I2∑x=0n∑y=0nfx,yφi1i2x,y.Among them,I1 represents the nonoverlapping square blocks in the visual image, I2 represents the overlapping square blocks in the visual image, φi1i2 represents the local information between adjacent pixels in the image, and fx,y represents the spatial neighborhood information of the visual image.According to the spatial neighborhood information of the visual image, the feature quantity of the visual image is extracted, the spatial distribution information of the visual image is obtained, and the performance of the adaptive planning of the visual image space is improved.


### 2.3. Three-Dimensional Rendering of Visual Images

Three-dimensional rendering technology is one of the cores of virtual reality technology. Rendering technology refers to the process of simulating the lighting of the physical environment and the texture of objects in the physical world in a three-dimensional scene to obtain a more realistic image. Rendering is not an independent concept. It is the process of bringing together all the work in the process of 3D model, texture, lighting, camera, and effect to form the final graphics sequence. Simply put, it is to create pixels that are given different colors to form a complete image. The rendering process requires a lot of complex calculations, which makes the computer busier. Currently, popular renderers support global illumination and HDRI technology, and simulations of caustics, depth of field, and 3S materials will also bring unexpected effects to rendering.


#### 2.3.1. Analysis of Light and Shadow Phenomena in Visual Image Rendering

In all aspects of rendering, light is the most important element. In order to better understand the principle of rendering, one must first understand the propagation mode of light in the real world: reflection, refraction, and transmission.(1). Reflection. Reflection is a very important factor that reflects the texture of an object. The reflection of light refers to the phenomenon that light hits the surface of an object and rebounds during movement. It includes diffuse reflection and specular reflection. All objects that can be seen are affected by these two methods. The first is color. When the object bounces all the light back, people will see that the object appears white. When the object absorbs all the light but does not bounce, the object will appear black. When the object only absorbs part of the light, then the rest of the light is absorbed. When bounced out, the object will show a variety of colors. For example, when the object only bounces red light and absorbs other light, the surface of the object will appear red. The second is gloss. Smooth objects will always have obvious highlights. For example, glass, porcelain, metal, but objects without obvious highlights are usually relatively rough, such as bricks, tiles, soil, and lawn lights. The generation of highlights is also the effect of light reflection, which is the effect of mirror reflection. Smooth objects have a mirror-like effect, which is very sensitive to the position and color of the light source. Therefore, the smooth surface of the object reflects the light source, which is the highlighted area of the surface of the object. The smoother the object, the smaller the highlight range and the higher the intensity.(2). Refraction. The refraction of light is a phenomenon that occurs in transparent objects. Due to the different densities of the material, the light will be deflected when it passes from one medium to another. Different transparent materials have different refractive indices, which is an important means of expressing transparent materials.(3). Transmission. In the real world, when light encounters a transparent object, part of the light will be bounced, while another part of the light will continue through the object. If the light is strong, the light will have a caustic effect after penetrating the object. If the object is a translucent material, the light will scatter inside the object, which is called “subsurface scattering”. For example, milk, cola, jade, skin, etc., all have this effect.It can be said that the texture of any object is represented by the above three light transmission methods. In the process of visual image design, according to the light and shadow phenomenon in nature, it can be applied to rendering, which can more truly express the rendering effect and improve the visual effect of the image.


#### 2.3.2. Visual Image Rendering Processing Based on Extend Shadow Map Algorithm

The key to the execution of the Extend Shadow map algorithm lies in the comparison of two depth values. The comparison process is implemented using alpha testing technology. Alpha test is to test the alpha channel value of each pixel in the visual image by setting conditions. When each pixel is about to be drawn if the alpha test is started, only the pixels whose alpha values meet the conditions will be finally drawn (strictly speaking, the pixels that meet the conditions will pass this test and proceed to the next test. Only when all tests are passed can the painting be carried out), and those that do not meet the conditions will not be drawn. This condition can be always pass (default), never pass, pass if it is greater than the set value, pass if it is less than the set value, pass if it is equal to the set value, pass if it is greater than or equal to the setting value, pass if it is less than or equal to the set value, and pass if it is not equal to the setting value. In visual image rendering, two depth values are stored in the alpha channel of the texture image. The specific image rendering process is shown in Figure1.Figure 1
Image rendering flowchart.According to Figure1, in the process of visual image rendering, the original image is first obtained, and the image texture and one-dimensional gradient texture are created; then the entire scene is rendered as the viewpoint to obtain the depth value, and the depth value is stored in the visual image alpha channel of the texture; then use the current viewpoint to render the scene to render the image texture.


## 2.1. Visual Image Wavelet Denoising

The wavelet transform method was used to denoise the visual image [6], assuming that the visual image contains the following noise signals:(1)Fn=ωn+αn.Among them,ωn represents the original signal of the visual image and αn represents the noise contained in the image.Discretize the noise signal in the visual image to obtain the discrete signalkn, where n=0,1,…,N, and use the wavelet transform method to sample some of the signals in the discrete signal:(2)Dj=1nX1j−AVGj+X2j−AVGj+,...,+Xnj−AVGj.Among them,Xnj represents the scale function, Dj represents the wavelet decomposition result of the visual image, and AVGj represents the wavelet coefficient. Further, transform the wavelet coefficients to obtain the following:(3)AVGj=1nX1j+X2j+,...,+Xnj.Assuming that the wavelet function isYij, the filter coefficient matrices corresponding to the scaling function Xnj and the wavelet function Yij are Znj and Lij, which are expressed by formulas (4) and (5), respectively:(4)Znj=1z11⋯z1j1z21⋯z2j⋮⋮⋮1zn1⋯znj,(5)Lij=l1i0⋯00l2i⋯⋮⋮⋮00⋯lij.According to formulas (4) and (5), the visual image is denoted as SZnj,Lij. According to the linear nature of wavelet transform, it can be seen that after discretizing SZnj,Lij, the image obtained is still composed of two parts; these are the coefficient Wk corresponding to the original signal ωn and the coefficient Xk corresponding to the noise signal αn.After the wavelet decomposition of the visual image, the normal signal in the image is highly concentrated on the wavelet coefficients with a larger amplitude, while the noise signal is randomly distributed in the transform domain. At this time, the noise in the wavelet coefficients can be further denoised. In reference [7], the specific denoising process is as follows:Step 1: select appropriate wavelet coefficients, decompose the visual image to be denoised, and obtain theo-level visual image hierarchyStep 2: choose an appropriate threshold and quantify different visual image levels [8]Step 3: according to the quantization processing result, the visual images of each layer are subjected to denoising processing to achieve the overall denoising result


## 2.2. Extraction of Visual Image Spatial Distribution Information

Based on the wavelet denoising of the visual image, the spatial planning of the visual image is carried out to obtain the adaptive spatial distribution value of the visual image:(6)Hμ=λN∑i=1Myi2.Among them,yi represents the gradient pattern of the pixel feature points of the visual image in the i direction. According to the visual scene, a binary model of fuzzy spatial vision fusion is established, and the correlation characteristic of the adaptive spatial visual feature matching of the visual image is obtained by using this model, which is expressed by the following formula:(7)Hμi×n=∑i=1ncirij.Among them,ci represents the texture feature of the visual image and rij represents the pixel array of the image.From this, the local spatial structure component of the visual image can be obtained. On this basis, the linear programming method [9] is further used to control the global spatial threshold ϖdi of the visual image:(8)ϖdi=∑i=1n∑j=1ntpi−opj.Among them,tpi represents the distance of the visual point and opj represents the image reference feature point.Perform visual tracking and matching of the histogram of the visual image with the reference feature points, and establish a multilayer segmentation model of the visual image space in the local area of the image:(9)Zi=∑i,j=1Nexp−di,jϖdi.In the visual image area, the Harris corner point detection method is used to mark the corner points of the image, and the spatial visual information fusion result of the visual image is obtained as follows:(10)Eκ=∑i,j=1nφμi,j×Zi.Among them,φμ represents the image texture coordinate space change rate and κ represents the image quantization coding.Use the edge contour feature extraction method to extract the spatial distribution information of the visual image in the fusion result:(11)Wφ=1I1I2∑x=0n∑y=0nfx,yφi1i2x,y.Among them,I1 represents the nonoverlapping square blocks in the visual image, I2 represents the overlapping square blocks in the visual image, φi1i2 represents the local information between adjacent pixels in the image, and fx,y represents the spatial neighborhood information of the visual image.According to the spatial neighborhood information of the visual image, the feature quantity of the visual image is extracted, the spatial distribution information of the visual image is obtained, and the performance of the adaptive planning of the visual image space is improved.


## 2.3. Three-Dimensional Rendering of Visual Images

Three-dimensional rendering technology is one of the cores of virtual reality technology. Rendering technology refers to the process of simulating the lighting of the physical environment and the texture of objects in the physical world in a three-dimensional scene to obtain a more realistic image. Rendering is not an independent concept. It is the process of bringing together all the work in the process of 3D model, texture, lighting, camera, and effect to form the final graphics sequence. Simply put, it is to create pixels that are given different colors to form a complete image. The rendering process requires a lot of complex calculations, which makes the computer busier. Currently, popular renderers support global illumination and HDRI technology, and simulations of caustics, depth of field, and 3S materials will also bring unexpected effects to rendering.


### 2.3.1. Analysis of Light and Shadow Phenomena in Visual Image Rendering

In all aspects of rendering, light is the most important element. In order to better understand the principle of rendering, one must first understand the propagation mode of light in the real world: reflection, refraction, and transmission.(1). Reflection. Reflection is a very important factor that reflects the texture of an object. The reflection of light refers to the phenomenon that light hits the surface of an object and rebounds during movement. It includes diffuse reflection and specular reflection. All objects that can be seen are affected by these two methods. The first is color. When the object bounces all the light back, people will see that the object appears white. When the object absorbs all the light but does not bounce, the object will appear black. When the object only absorbs part of the light, then the rest of the light is absorbed. When bounced out, the object will show a variety of colors. For example, when the object only bounces red light and absorbs other light, the surface of the object will appear red. The second is gloss. Smooth objects will always have obvious highlights. For example, glass, porcelain, metal, but objects without obvious highlights are usually relatively rough, such as bricks, tiles, soil, and lawn lights. The generation of highlights is also the effect of light reflection, which is the effect of mirror reflection. Smooth objects have a mirror-like effect, which is very sensitive to the position and color of the light source. Therefore, the smooth surface of the object reflects the light source, which is the highlighted area of the surface of the object. The smoother the object, the smaller the highlight range and the higher the intensity.(2). Refraction. The refraction of light is a phenomenon that occurs in transparent objects. Due to the different densities of the material, the light will be deflected when it passes from one medium to another. Different transparent materials have different refractive indices, which is an important means of expressing transparent materials.(3). Transmission. In the real world, when light encounters a transparent object, part of the light will be bounced, while another part of the light will continue through the object. If the light is strong, the light will have a caustic effect after penetrating the object. If the object is a translucent material, the light will scatter inside the object, which is called “subsurface scattering”. For example, milk, cola, jade, skin, etc., all have this effect.It can be said that the texture of any object is represented by the above three light transmission methods. In the process of visual image design, according to the light and shadow phenomenon in nature, it can be applied to rendering, which can more truly express the rendering effect and improve the visual effect of the image.


### 2.3.2. Visual Image Rendering Processing Based on Extend Shadow Map Algorithm

The key to the execution of the Extend Shadow map algorithm lies in the comparison of two depth values. The comparison process is implemented using alpha testing technology. Alpha test is to test the alpha channel value of each pixel in the visual image by setting conditions. When each pixel is about to be drawn if the alpha test is started, only the pixels whose alpha values meet the conditions will be finally drawn (strictly speaking, the pixels that meet the conditions will pass this test and proceed to the next test. Only when all tests are passed can the painting be carried out), and those that do not meet the conditions will not be drawn. This condition can be always pass (default), never pass, pass if it is greater than the set value, pass if it is less than the set value, pass if it is equal to the set value, pass if it is greater than or equal to the setting value, pass if it is less than or equal to the set value, and pass if it is not equal to the setting value. In visual image rendering, two depth values are stored in the alpha channel of the texture image. The specific image rendering process is shown in Figure1.Figure 1
Image rendering flowchart.According to Figure1, in the process of visual image rendering, the original image is first obtained, and the image texture and one-dimensional gradient texture are created; then the entire scene is rendered as the viewpoint to obtain the depth value, and the depth value is stored in the visual image alpha channel of the texture; then use the current viewpoint to render the scene to render the image texture.


## 2.3.1. Analysis of Light and Shadow Phenomena in Visual Image Rendering

In all aspects of rendering, light is the most important element. In order to better understand the principle of rendering, one must first understand the propagation mode of light in the real world: reflection, refraction, and transmission.(1). Reflection. Reflection is a very important factor that reflects the texture of an object. The reflection of light refers to the phenomenon that light hits the surface of an object and rebounds during movement. It includes diffuse reflection and specular reflection. All objects that can be seen are affected by these two methods. The first is color. When the object bounces all the light back, people will see that the object appears white. When the object absorbs all the light but does not bounce, the object will appear black. When the object only absorbs part of the light, then the rest of the light is absorbed. When bounced out, the object will show a variety of colors. For example, when the object only bounces red light and absorbs other light, the surface of the object will appear red. The second is gloss. Smooth objects will always have obvious highlights. For example, glass, porcelain, metal, but objects without obvious highlights are usually relatively rough, such as bricks, tiles, soil, and lawn lights. The generation of highlights is also the effect of light reflection, which is the effect of mirror reflection. Smooth objects have a mirror-like effect, which is very sensitive to the position and color of the light source. Therefore, the smooth surface of the object reflects the light source, which is the highlighted area of the surface of the object. The smoother the object, the smaller the highlight range and the higher the intensity.(2). Refraction. The refraction of light is a phenomenon that occurs in transparent objects. Due to the different densities of the material, the light will be deflected when it passes from one medium to another. Different transparent materials have different refractive indices, which is an important means of expressing transparent materials.(3). Transmission. In the real world, when light encounters a transparent object, part of the light will be bounced, while another part of the light will continue through the object. If the light is strong, the light will have a caustic effect after penetrating the object. If the object is a translucent material, the light will scatter inside the object, which is called “subsurface scattering”. For example, milk, cola, jade, skin, etc., all have this effect.It can be said that the texture of any object is represented by the above three light transmission methods. In the process of visual image design, according to the light and shadow phenomenon in nature, it can be applied to rendering, which can more truly express the rendering effect and improve the visual effect of the image.


## 2.3.2. Visual Image Rendering Processing Based on Extend Shadow Map Algorithm

The key to the execution of the Extend Shadow map algorithm lies in the comparison of two depth values. The comparison process is implemented using alpha testing technology. Alpha test is to test the alpha channel value of each pixel in the visual image by setting conditions. When each pixel is about to be drawn if the alpha test is started, only the pixels whose alpha values meet the conditions will be finally drawn (strictly speaking, the pixels that meet the conditions will pass this test and proceed to the next test. Only when all tests are passed can the painting be carried out), and those that do not meet the conditions will not be drawn. This condition can be always pass (default), never pass, pass if it is greater than the set value, pass if it is less than the set value, pass if it is equal to the set value, pass if it is greater than or equal to the setting value, pass if it is less than or equal to the set value, and pass if it is not equal to the setting value. In visual image rendering, two depth values are stored in the alpha channel of the texture image. The specific image rendering process is shown in Figure1.Figure 1
Image rendering flowchart.According to Figure1, in the process of visual image rendering, the original image is first obtained, and the image texture and one-dimensional gradient texture are created; then the entire scene is rendered as the viewpoint to obtain the depth value, and the depth value is stored in the visual image alpha channel of the texture; then use the current viewpoint to render the scene to render the image texture.


## 3. Visual Image Reconstruction Based on Virtual Reality

Based on the spatial distribution information of the visual image and the three-dimensional rendering result of the visual image, virtual reality technology is used to reconstruct the visual image. Virtual reality is a technology that can simulate all kinds of materials in space very realistically. Through the human-computer interface technology, we can freely observe the surrounding scenery and use special equipment to interact with virtual objects, which can make the image have a very realistic visual effect and fully meet the standard of visual image reconstruction.In order to solve the problem of poor visual image detail processing in traditional visual art design, a visual image reconstruction method based on virtual reality is proposed. Wavelet technology is introduced to decompose the visual image, quantify the visual image, and compress the image to obtain different quality image resources. According to the virtual reality measurement, different image resources are classified to complete the multimedia image reconstruction.Use the irregular triangulation method to realize the reconstruction of the visual image. First, obtain the boundary information of the mesh model, and then visually track and measure the seed point according to the angle of wavelet diffusion, and match the tracked trajectory with features, and obtain different discrete sampling points. Use the cropping method to track each grid line and then obtain the corresponding points of the image tracking trajectory reconstruction. In the distributed scene of the target, the visual image rendering technology is used to realize the three-dimensional imaging of the image trajectory. Finally, modeling is performed. In the virtual scene, the data of the visual image is input into the 3D model to obtain the initial position and posture information, and the image is reconstructed virtually in reality. The actual block diagram of the reconstruction is shown in Figure2.Figure 2
Frame diagram of visual image reconstruction.According to Figure2, we first complete the graphical interface setting and establish a static three-dimensional virtual model library combined with the large scene terrain, and then analyze the image environment, initial position, special effects design, application Settings, and transmit the files in the image. The corresponding programs include driving algorithm, data processing, image collision detection and response, scene scheduling and management, scene rendering, device output, etc., to achieve visual image reconstruction.According to the above steps, the visual image reconstruction based on virtual reality is completed. Based on this, the visual image is further optimized and the final visual image design result is obtained.


## 4. Optimization of Visual Image Imaging Results

In order to better present the imaging results of the visual image design, the image reconstruction needs to be optimized. In this paper, the image segmentation method is used to optimize the reconstruction results so that the image area division is clearer and the feature points are more prominent.At this stage, the more common image segmentation methods are the variance method and directional image method. The former is mainly based on the gray characteristics of the image, which is more suitable for the segmentation of the image background area. However, this method has one disadvantage, that is, it is easy to lead to misjudgment for areas with small gray change [10]. The latter is mainly based on the direction information of the image. This method has a good segmentation effect for the small change area, but the segmentation effect for the background area is not very ideal [11]. Aiming at the problems of the abovementioned traditional methods, this paper uses the ant colony algorithm to optimize the visual image. The image segmentation method based on the ant colony algorithm comprehensively considers the grayscale, gradient, and neighborhood characteristics of each pixel in the image [12]. Using the fuzzy clustering ability of the ant colony algorithm, image segmentation is regarded as a process of clustering pixels with different characteristics.Given the original imageU, consider each pixel uii=1,2,…,n as an ant, and each ant is a three-dimensional vector characterized by grayscale, gradient, and neighborhood. Image segmentation is the process by which these ants with different characteristics search for food sources. The distance between any pixel u1 and u2 is du1u2, and the Euclidean distance between the two is calculated:(12)du1u2=∑r=1nDijru1−ru2.Among them,r represents the feature dimension of the ant colony and Dij represents the weighting factor, and the value of this parameter is determined by the degree of influence of each component of the pixel on the cluster.SetR as the cluster radius and ψk as the amount of information contained in the image, then(13)ψk=1,Dij≤R,0,Dij>R.The probability thatu1 chooses the path to u2 is as follows:(14)Dk=12∑i′j′nwTui′′−wTuj′×di′j′.Among them,di′j′ represents the set of feasible paths. After the above cycle, the amount of information on each path can be adjusted by the following formula:(15)ψk=exp−u1−m2+u2−n2σ2.Among them,σ2 represents the similarity between pixels.According to the above analysis, the optimization process of visual image imaging results based on ant colony algorithm is as follows:(1)
Convert the image data into a matrix [13]; each data in the matrix corresponds to an ant.(2)
Initialize the parameters, set the timeT=0 and the number of cycles τ=0, and set the maximum number of cycles to τmax.(3)
Start the clustering cycle, and the number of cycles gradually increases by 1, and at the same time, the number of ant colonies continues to increase.(4)
Calculate the distance from pixelu1 to u2 according to formula (12) [14]. If the distance is zero, the degree of membership of the pixel to this class is 1.(5)
According to formula (15), calculate the amount of information of each path from u1 to u2.(6)
Adjust the amount of information on the path and update the cluster center.(7)
If the end condition is met, that is, the number of cyclesτ≥τmax, the cycle [15] is ended, and the calculation result is output. Otherwise, go to step (3).In summary, the preprocessing of visual images is achieved through visual image wavelet denoising, visual image spatial distribution information extraction, and visual image three-dimensional rendering, which provides preconditions for visual image design. Then use virtual reality technology to reconstruct the preprocessed image and further use the ant colony algorithm to segment the reconstructed image so that the image area is more clearly divided, the feature points are more prominent, and the goal of visual image optimization is realized [16].


## 5. Experimental Research

In order to verify the effectiveness of the visual art design method based on virtual reality and verify the effect of visual image processing under the method, a simulation experiment is set up. The image optimization design method based on the visual cognition theory and the visual optimization design method based on computer interaction technology are used as the contrast method, and the advantages of the visual art design method based on virtual reality are verified through comparative analysis.


### 5.1. Experimental Data Settings

The images used in the experiment are all from the ImageNet database, which is the largest known image database at present and contains a wide range of images. ImageNet is an image dataset organized according to the WordNet hierarchical structure. 600 images of different types are selected from the database [17]. There are 6 datasets, and the parameters of each dataset are shown in Table 1.Table 1
Experimental dataset.
Dataset numberNumber of imagesData dimension1154629710355541038587661044According to the above experimental conditions, the visual image processing and design results of different methods are compared, and the experimental conclusions are drawn.


### 5.2. Analysis of Experimental Results

#### 5.2.1. Visual Image Processing Effects under Different Lighting Conditions

With normal light, strong light, and weak light as the preconditions, 5 pictures are selected arbitrarily in the experimental dataset, and the peak signal-to-noise ratio is used as the evaluation index. The calculation formula is as follows:(16)PSNR=10×log10ImaxMSE.The visual image processing effects of different methods are compared, and the results are shown in Table2.Table 2
Comparison of peak signal-to-noise ratio of different methods.
MethodLighting conditionsImage 1Image 2Image 3Image 4Image 5Visual art design method based on virtual realityNormal light201.47161.52190.30162.01138.76Strong light161.88116.10139.56116.91128.55Weak light89.0681.3981.9088.5675.42Image optimization design method based on sparse representation algorithmNormal light135.64123.56176.35111.69134.26Strong light115.3496.32107.2991.2484.23Weak light86.3269.3269.8272.6962.39Image optimization design method based on dynamic visual communicationNormal light128.12121.58137.26128.46104.54Strong light101.36109.2191.3274.1378.12Weak light78.2869.9277.2365.2467.11Image optimization design method based on visual cognition theoryNormal light123.63119.25151.06136.1790.54Strong light104.2589.25101.3672.1570.19Weak light75.2663.9779.6360.2557.18Visual optimization design method based on computer interactive technologyNormal light147.58114.36105.5896.36111.23Strong light143.2296.3396.37124.47105.99Weak light54.1960.2376.3964.2863.59The higher the PSNR, the better the image processing effect. According to the data in Table2, under different lighting conditions, the peak signal-to-noise ratio of visual art design method based on virtual reality is higher than that of image optimization design method based on visual cognition theory and visual optimization design method based on computer interaction technology. The peak signal-to-noise ratio of the visual art design method based on virtual reality reaches 201.47, which is much higher than the existing methods, indicating that this method can effectively improve the quality of the visual image.


#### 5.2.2. Visual Image Optimization Effect

A street view image is selected arbitrarily in the experimental dataset as the research object, and the visual art design method based on virtual reality is used to optimize it. The result is shown in Figure3.Figure 3
Comparison of image optimization effects. (a) Original image. (b) Optimized image.
(a)(b)According to Figure3, Figure 3(a) is the original image. After optimization, the effect color of Figure 3(b) changes, and the color is brighter. It shows that the visual art design method based on virtual reality can accurately estimate the color in the process of visual image optimization, and the optimization effect is better.In order to further verify the application effect of the visual art design method based on virtual reality, compare the visual image optimization effects of different methods. Similarly, an image is selected as the experimental object in the experimental dataset, and different methods are used to optimize the image. The results are shown in Figure4.Figure 4
Comparison of visual image optimization effects of different methods. (a) Original image. (b) Visual art design method based on vertical reality. (c) Image optimization design method based on sparse representation algorithm. (d) Image optimization design method based on dynamic visual communication. (e) Image optimization design method based on dynamic visual cognition theory. (f) Visual optimization design method based on computer interactive technology.
(a)(b)(c)(d)(e)(f)According to Figure4, the visual optimization design method based on computer interaction technology has a small amount of calculation and good stability, but the image segmentation is inaccurate, some spatial information is ignored, and the edge detection result is inaccurate. The segmentation effect of the image optimization design method based on the visual cognitive theory is better, but the image details are too much, and the phenomenon of overexposure occurs. The image optimization effect of the visual art design method based on virtual reality is ideal, the image details are kept intact, and the image background is visually improved.


## 5.1. Experimental Data Settings

The images used in the experiment are all from the ImageNet database, which is the largest known image database at present and contains a wide range of images. ImageNet is an image dataset organized according to the WordNet hierarchical structure. 600 images of different types are selected from the database [17]. There are 6 datasets, and the parameters of each dataset are shown in Table 1.Table 1
Experimental dataset.
Dataset numberNumber of imagesData dimension1154629710355541038587661044According to the above experimental conditions, the visual image processing and design results of different methods are compared, and the experimental conclusions are drawn.


## 5.2. Analysis of Experimental Results

### 5.2.1. Visual Image Processing Effects under Different Lighting Conditions

With normal light, strong light, and weak light as the preconditions, 5 pictures are selected arbitrarily in the experimental dataset, and the peak signal-to-noise ratio is used as the evaluation index. The calculation formula is as follows:(16)PSNR=10×log10ImaxMSE.The visual image processing effects of different methods are compared, and the results are shown in Table2.Table 2
Comparison of peak signal-to-noise ratio of different methods.
MethodLighting conditionsImage 1Image 2Image 3Image 4Image 5Visual art design method based on virtual realityNormal light201.47161.52190.30162.01138.76Strong light161.88116.10139.56116.91128.55Weak light89.0681.3981.9088.5675.42Image optimization design method based on sparse representation algorithmNormal light135.64123.56176.35111.69134.26Strong light115.3496.32107.2991.2484.23Weak light86.3269.3269.8272.6962.39Image optimization design method based on dynamic visual communicationNormal light128.12121.58137.26128.46104.54Strong light101.36109.2191.3274.1378.12Weak light78.2869.9277.2365.2467.11Image optimization design method based on visual cognition theoryNormal light123.63119.25151.06136.1790.54Strong light104.2589.25101.3672.1570.19Weak light75.2663.9779.6360.2557.18Visual optimization design method based on computer interactive technologyNormal light147.58114.36105.5896.36111.23Strong light143.2296.3396.37124.47105.99Weak light54.1960.2376.3964.2863.59The higher the PSNR, the better the image processing effect. According to the data in Table2, under different lighting conditions, the peak signal-to-noise ratio of visual art design method based on virtual reality is higher than that of image optimization design method based on visual cognition theory and visual optimization design method based on computer interaction technology. The peak signal-to-noise ratio of the visual art design method based on virtual reality reaches 201.47, which is much higher than the existing methods, indicating that this method can effectively improve the quality of the visual image.


### 5.2.2. Visual Image Optimization Effect

A street view image is selected arbitrarily in the experimental dataset as the research object, and the visual art design method based on virtual reality is used to optimize it. The result is shown in Figure3.Figure 3
Comparison of image optimization effects. (a) Original image. (b) Optimized image.
(a)(b)According to Figure3, Figure 3(a) is the original image. After optimization, the effect color of Figure 3(b) changes, and the color is brighter. It shows that the visual art design method based on virtual reality can accurately estimate the color in the process of visual image optimization, and the optimization effect is better.In order to further verify the application effect of the visual art design method based on virtual reality, compare the visual image optimization effects of different methods. Similarly, an image is selected as the experimental object in the experimental dataset, and different methods are used to optimize the image. The results are shown in Figure4.Figure 4
Comparison of visual image optimization effects of different methods. (a) Original image. (b) Visual art design method based on vertical reality. (c) Image optimization design method based on sparse representation algorithm. (d) Image optimization design method based on dynamic visual communication. (e) Image optimization design method based on dynamic visual cognition theory. (f) Visual optimization design method based on computer interactive technology.
(a)(b)(c)(d)(e)(f)According to Figure4, the visual optimization design method based on computer interaction technology has a small amount of calculation and good stability, but the image segmentation is inaccurate, some spatial information is ignored, and the edge detection result is inaccurate. The segmentation effect of the image optimization design method based on the visual cognitive theory is better, but the image details are too much, and the phenomenon of overexposure occurs. The image optimization effect of the visual art design method based on virtual reality is ideal, the image details are kept intact, and the image background is visually improved.


## 5.2.1. Visual Image Processing Effects under Different Lighting Conditions

With normal light, strong light, and weak light as the preconditions, 5 pictures are selected arbitrarily in the experimental dataset, and the peak signal-to-noise ratio is used as the evaluation index. The calculation formula is as follows:(16)PSNR=10×log10ImaxMSE.The visual image processing effects of different methods are compared, and the results are shown in Table2.Table 2
Comparison of peak signal-to-noise ratio of different methods.
MethodLighting conditionsImage 1Image 2Image 3Image 4Image 5Visual art design method based on virtual realityNormal light201.47161.52190.30162.01138.76Strong light161.88116.10139.56116.91128.55Weak light89.0681.3981.9088.5675.42Image optimization design method based on sparse representation algorithmNormal light135.64123.56176.35111.69134.26Strong light115.3496.32107.2991.2484.23Weak light86.3269.3269.8272.6962.39Image optimization design method based on dynamic visual communicationNormal light128.12121.58137.26128.46104.54Strong light101.36109.2191.3274.1378.12Weak light78.2869.9277.2365.2467.11Image optimization design method based on visual cognition theoryNormal light123.63119.25151.06136.1790.54Strong light104.2589.25101.3672.1570.19Weak light75.2663.9779.6360.2557.18Visual optimization design method based on computer interactive technologyNormal light147.58114.36105.5896.36111.23Strong light143.2296.3396.37124.47105.99Weak light54.1960.2376.3964.2863.59The higher the PSNR, the better the image processing effect. According to the data in Table2, under different lighting conditions, the peak signal-to-noise ratio of visual art design method based on virtual reality is higher than that of image optimization design method based on visual cognition theory and visual optimization design method based on computer interaction technology. The peak signal-to-noise ratio of the visual art design method based on virtual reality reaches 201.47, which is much higher than the existing methods, indicating that this method can effectively improve the quality of the visual image.


## 5.2.2. Visual Image Optimization Effect

A street view image is selected arbitrarily in the experimental dataset as the research object, and the visual art design method based on virtual reality is used to optimize it. The result is shown in Figure3.Figure 3
Comparison of image optimization effects. (a) Original image. (b) Optimized image.
(a)(b)According to Figure3, Figure 3(a) is the original image. After optimization, the effect color of Figure 3(b) changes, and the color is brighter. It shows that the visual art design method based on virtual reality can accurately estimate the color in the process of visual image optimization, and the optimization effect is better.In order to further verify the application effect of the visual art design method based on virtual reality, compare the visual image optimization effects of different methods. Similarly, an image is selected as the experimental object in the experimental dataset, and different methods are used to optimize the image. The results are shown in Figure4.Figure 4
Comparison of visual image optimization effects of different methods. (a) Original image. (b) Visual art design method based on vertical reality. (c) Image optimization design method based on sparse representation algorithm. (d) Image optimization design method based on dynamic visual communication. (e) Image optimization design method based on dynamic visual cognition theory. (f) Visual optimization design method based on computer interactive technology.
(a)(b)(c)(d)(e)(f)According to Figure4, the visual optimization design method based on computer interaction technology has a small amount of calculation and good stability, but the image segmentation is inaccurate, some spatial information is ignored, and the edge detection result is inaccurate. The segmentation effect of the image optimization design method based on the visual cognitive theory is better, but the image details are too much, and the phenomenon of overexposure occurs. The image optimization effect of the visual art design method based on virtual reality is ideal, the image details are kept intact, and the image background is visually improved.


## 6. Conclusion

Aiming at the problem of poor detail processing effect of traditional visual images, a visual art design method based on virtual reality is proposed. Through visual image denoising, image information extraction, and image rendering, the visual image is preprocessed, and then virtual reality technology is used to reconstruct the preprocessed image, and then the ant colony algorithm is used to segment the reconstructed image. The image area is divided more clearly, the feature points are more prominent, and the purpose of visual image optimization is realized. The results show that the visual art design method based on virtual reality has a better image processing effect and high peak signal-to-noise ratio, which fully verifies the visual image processing effect of this method and shows that it has a very important significance in the field of the visual art design.



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*Source: 1014017-2021-08-20.xml*