# Research on Multichannel 3D Oil Painting Style Rendering Model Based on 3DS Max

Authors: Zinan Liu
Journal: Mobile Information Systems
 (2022)

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## Abstract

People are becoming less comfortable in content as a form of data throughout this period of growing scientific and technological advancements. Oil painting also was challenged in unprecedented ways by modern image processing techniques as person’s primary source of data, and it had a tremendous effect on the domain of artistic creativity. Therefore, we present an upgraded convolutional neural network (U-CNN) for the enhancement of style rendering model addressing the aforementioned challenges. Initially, the datasets are gathered that are denoised and enhanced in the preprocessing stage to eliminate noise and improve the quality of the data. Gabor filter bank (GFB) is employed in the feature extraction stage to extract several features from the normalized data. For the application of style rendering model, the proposed approach is utilized. Moreover, through applying the 3DS MAX model, the three-dimensional (3-D) oil painting is generated. Finally, the performance of the proposed approach is examined and compared with other existing approaches to obtain the proposed approach with the greatest effectiveness. The findings are depicted in chart form by employing the origin tool.


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## Body

## 1. Introduction

Historic and national value of oil painting art form its artistic and cultural diffusion and digital inheritance importance. Oil painting art supplies include national traditional culture, national spirit, and aesthetic appeal. The cross-cultural spread and inheritance of oil painting art has new opportunities and challenges as cultural exchanges increase in many areas of contemporary society. Oil paintings are kept for years. Toxic substances such as lead and mercury can induce discoloration and yellowing. These features will limit the spread of oil paintings. Also, cultural variations and recognition will impede optimal spread. Globalization and local culture have become hot topics for society and scholars as a result of the complex background of multiculturalism, rapid development of information technology, and integration and protection of globalization and local culture. Oil painting’s beauty lies in its many visual interpretations and amazing methods. People’s expertise and sweat has created thousands of years of history in oil painting techniques. Colors used in transparent overcoloring or multilayer drawing are not mixed with white but diluted by toner. After each layer becomes dry, color the next layer. Because each layer’s color is thinner, the bottom layer’s color may be visible, resulting in a subtle tint that fluctuates with the top layer’s color. Opaque overcoloring is also called layered coloring. A single color is used to draw the body’s contour, and subsequently colors are utilized to sculpt it in layers. Dark portions are painted thinner, whereas medium and light areas are painted thicker, covered, or left [1].Color is a vital characteristic of real 3D objects that has proven challenging to replicate. Additive manufacturing color digitalization enhances the realistic aspects of 3D fabrications in several fields. Material jetting is one of seven polymer additive manufacturing methods. Three to six major inks and even more spot colors have been studied for application in color 3d modeling. But traditional manual copying and graphics printing methods struggle to correctly duplicate minute components of an oil painting. As a result, the small variations in the digital capture of the oil painting are lost. Oil paintings may now be 3D scanned thanks to the development of microscopic 3D scanning devices and improved acquisition algorithms. The intricate color information in the color 3D model produced from 3D scan data of the oil painting and current color 3D printers’ maximum print accuracy restrictions cause the stuck staircase effect [2]. If utilized successfully, technology may be a vital element of the educational environment. As schools begin to employ 3D printers, a new creative process emerges. This technology may be used in classrooms to encourage student engagement and curiosity. Students are driven to produce new ideas and procedures when they are studying and generating something practical connected to their studies. It is important to recognize the importance of integration and cooperation in education. The rising usage of technological gadgets by students may also lead to fresh ideas. These discoveries help us think about how 3D printing might boost learning. Art enhances the curriculum and its involvement may extend educational ideas. Children and adults alike enjoy creative crafts. When grownups are happy, children are happy. Teachers may advise students and help them to generate creative and original work [3]. The auto encoder and generative adversarial network (GAN) are two outstanding picture generating methods that have evolved alongside deep neural networks in computer vision. Pix2pix learned the transformation rule in a number of paired photos. Cycle GAN overcame the need for paired pictures by learning common characteristics in two sets of classified photos. It created oil paintings from still photos and replaced horses with zebras [4]. Meanwhile, demand for high-fidelity oil paintings grows, yet conventional manual copying and graphics printing technologies struggle to precisely recreate minute elements of an oil painting. As a result, the important signatures from the different tiny variances in the digital capture of the oil painting are lost. Small 3D scanning machines allow quick and precise 3D scanning of oil paintings. The intricate color information in the color 3D model created from 3D scan data of the oil painting and current color 3D printers’ maximum print accuracy restrictions cause the stuck staircase phenomenon [5]. So, we provide an upgraded convolutional neural network (U-CNN) for style rendering model enhancement. The data is collected first, then denoised and upgraded in preprocessing to remove noise and increase data quality. In the feature extraction step, PCA is used to extract many features from the normalized data. The suggested technique is used for style rendering model.The remainder of the description is divided into 4 parts: Section 2: related works and problem definition. Section 3: the proposed methodology. Segment 4: result and discussion. Segment 5: conclusion.


## 2. Related Works

In [6], many of the disorders have multiple odontogenic keratocysts. A 12-year-old female youngster had several odontogenic keratocysts. The studies found no other anomalies indicative of a condition. In [7], personalized medicine employs fine-grained data to identify specific deviations from normal. These developing data-driven health care methods were conceptually and ethically investigated using ‘digital twins’ within engineering. Physical artifacts were coupled using digital techniques that continuously represent their state. Moral differences can be observed based on data structures and interpretations imposed on them. Digital twins’ ethical and sociological ramifications are examined. Healthcare system has become increasingly data-driven. This technique could be a social equalizer through providing for efficient equalizing enhancing strategies. In [8], allergic rhinitis would be a long-standing worldwide epidemic. Taiwanese doctors commonly treat it with either traditional Chinese or Chinese–Western drugs. Outpatient traditional Chinese medicine therapy of respiratory illnesses was dominated by allergic rhinitis. They compare traditional Chinese medicine with western medical therapies treating allergic rhinitis throughout Taiwan. In [9], the usage of high-dose-rate (HDR) brachytherapy avoids radioactivity, allowing for outpatient therapy, and reduces diagnosis timeframes. A single-stepping source could also enhance dosage dispersion by adjusting latency at every dwell location. The shorter processing intervals need not permit any error checking, and inaccuracies could injure individuals. Hence, HDR brachytherapy therapies should be performed properly. In [10], this study presented a treatment as well as technology of domestic sewage to improve the rural surroundings. In [11], soil samples from chosen vegetable farms throughout Zamfara State, Nigeria have been tested for physicochemical and organochlorine pesticides. Testing procedure and data were analyzed using QuEChERS with GC-MS. In [12], the goal was to focus on the use of 3D imaging technologies to rebuild Impressionist oil paintings. Three-dimensional reconstruction using point cloud (PB) was examined. In [13], this study presents a new oil painting replication process utilizing 3D printing. First, photogrammetric completes 3D reconstruction of oil paintings; color, and 3D geometric information are better recovered by capturing several orthophotomaps, and modeling correctness is assured by a control mesh or flattening. The major purpose of [14] was to correctly identify this great painter’s execution style, palette, and any undocumented interventions. For this part, the 3D photogrammetric survey enabled us to noninvasively assess the extent of restored regions and appropriately map the domains of the various canvasses seen. In [15], the authors suggested that the multianalytical strategy was successful in obtaining iconographic, morphological, structural, and compositional data for both artworks. The data gathered not only help preserve and save the artworks but also reveal that Févère used the same large-scale preliminary sketch (carton) that Rubens used to prepare his oil paints for painting. In [16], since 3D ink painting rendering model has poor ink concentration control and model creation efficiency, a multichannel 3D ink painting rendering model based on the Least Mean Square (LMS) algorithm is created. The local search method is utilized to get ink painting characteristics. The LMS algorithm filters the original ink drawing data. Iterative computation allows for dynamic depiction of 3D ink paintings. The goal of [17] was to optimize the oil painting creation. It is apparent that digital image technology and oil painting production are strongly connected and complimented. An emphasis on using computer graphics technology to several common oil painting methods makes the finished works more attractive and creative, and promotes the use of image processing and computer-aided technology in oil painting production. In [18], the article presents an ink-wash painting technique for producing 3D sceneries including mountains, fog, and water. This approach generated feature lines from mountain models based on curvature and replicated the texture of Xuan paper and Juan. To complete the scenario, they added fog and water wave effects. In [19], the authors suggested that the industrial 3D printing becomes increasingly common, it’s time to talk about high-fidelity 3D object replication, especially in colors. Only a few studies have looked at exact color reproduction and universal color reproduction methods for 3D printing materials. In [20], aspects of data gathering and preparing data fusion are the subject of this work. A new 3D technology based on multiple laser sources will be described in order to compare it to the current devices and techniques widely used in cultural heritage and to show how research might try to combine gamification with diagnostic and restoration support in this field.


### 2.1. Problem Statement

In addition, since 3D printers use more energy than conventional production equipment, the cost of operating the machine might be greater than with traditional gear. The following materials should be considered: the cost of materials is often the most expensive component of the 3D printing process. 3D modeling is the act of creating a mathematical representation of an item or surface in the manner in which it would look if measured in terms of width, breadth, and depth. 3D rendering is the process of converting 3D models into visuals that are low-quality, less detailed, and unrealistic. High RAM utilization may cause your scene to crash or render too slowly. Just the software has to write/read temporary data from a hard disc. Hard drives are 1000 times slower than RAM, even solid state drives (SSDs).


## 2.1. Problem Statement

In addition, since 3D printers use more energy than conventional production equipment, the cost of operating the machine might be greater than with traditional gear. The following materials should be considered: the cost of materials is often the most expensive component of the 3D printing process. 3D modeling is the act of creating a mathematical representation of an item or surface in the manner in which it would look if measured in terms of width, breadth, and depth. 3D rendering is the process of converting 3D models into visuals that are low-quality, less detailed, and unrealistic. High RAM utilization may cause your scene to crash or render too slowly. Just the software has to write/read temporary data from a hard disc. Hard drives are 1000 times slower than RAM, even solid state drives (SSDs).


## 3. Proposed Work

In this research, we introduce a U-CNN. This is followed by preprocessing to remove noise and improve data quality. Several features are extracted from the normalized data using GFB. The proposed approach is used for style rendering. Moreover, the 3D oil painting is generated using the 3DS MAX model. Figure1 depicts the complete process of this research.Figure 1
Complete procedure of the research.


### 3.1. Dataset

A total of 7,500 traditional Chinese paints (TCPs) as well as 8,800 oil paintings (OPs) were used in the research [21]. A new class is created for every painting. There will be 156 images in every class after the image enhancement is complete. The statistics now have 1,175,000 and 1,375,000 subjects as samples.


### 3.2. Data Preprocessing

#### 3.2.1. Denoising Using Median Filter

In digital imaging, MF is a nonlinear filtration that has been employed to eliminate the distortion from the dataset. MF is being used frequently because it can keep the edge when eliminating distortion under certain circumstances. Filtering a picture window-wise with MF replaces every element with the midpoint of the next closest ones. MF is a nonlinear smoothness approach that completely nullifies noise while still preserving the edge for certain noises (like random noise and salt-pepper noise). The edge of melanoma is still the most crucial aspect because most cancerous cells have a nodular shape. For an effective diagnosis, the shape of the edge contains critical data. As a result, the MF is critical in preprocessing because it preserves the edge design. Figure2 shows the functioning of MF (median filter).Figure 2
Function of median filter.


#### 3.2.2. Contour-Based Image Enhancement (CIE)

CIE is an important key for outlines. By using CIE, the boundary of the skin lesion can be retrieved. The lesion portion of the digital picture is removed from the contour. Finally, the actual image and skin lesions are generated by combining the binary image of the diseased part and also the actual image. It could follow the directions of moving spatially and temporally. This approach is particularly useful in the domain of medical imaging since it aids to increase contrast, particularly whenever the ROI and surroundings have similar contrast values. The contrast augmentation index (CAI) formula is used to describe the image’s contrast as a parameter.(1)CAI=CprocssedCactual,where Cprocssed represents value of the contrast of the processed image and Cactual represents value of the contrast of the actual image.(2)Image area contrast=m−sm+s,where m represents gray-level value of the “foreground” of the image and s represents gray-level value of the “background” of the image.


### 3.3. Feature Extraction Using Gabor Filter Bank (GFB)

GFB is commonly employed to retrieve characteristics in painting pictures, it could retrieve the spatial/frequency data. Amplitude, phases, and direction are the 3 kinds of characteristics created by the GFB. A sine plane wave modulates a Gaussian envelop in such filter banks. Throughout the spatial region, the Gabor filter is stated as follows:(3)Sx,y=fc2πtnefn2/t2x2+fn2/n2y2ej2πfcx′.Here,(4)x′=xcosθd+ysinθd,y′=−xsinθd+ycosθd,fc=fmax2c/2,θd=dπ8,where fc is centre frequency (fmax = ¼) and θd is direction.The proportion of the center frequency is evaluated to the size of Gaussian envelope usingt as well as n. Here, t=n=2 has been the most widely used variables.Throughout this research, we employed a bank of filters with 5 scales and 8 directions to retrieve distinct information from the painting pictures,c = 1 to 4 and d = 0 to 7.ConsiderI(x, y) become a grey-scale painting picture, and the feature extraction process is as follows:(5)Gc,dx,y=Ix,y∗Sc,dx,y,where Gc,dx,y represents complex filtering result.


### 3.4. Application of Style Rendering Model Using CNN

#### 3.4.1. Image Style Rendering Procedure

The image style rendering methodology is split into 2 phases: training and executing. Transform network models are trained on each of the style mapsYs that are selected during the training stage. Each time the training set is rerun, a new set of samples is generated for the content map. Iteration after iteration, X and Y are randomly transported from the conversion style structure to the discriminant structure D. By comparing the adversarial loss functions, network D determines whether the content is X or Y and if the style is Ys or Y, and then feeds that information back to the network T. T then makes any necessary alterations to the weights and settings before moving on to the next iteration. A continual process of optimization takes place in network D in order to uncover more disparities. The ultimate goal is to create a Y style image conversion network model. Real-time conversion of any content map to a Y style-impact picture is achieved by feeding it into a model that has been well-trained. This means that the real underpinnings stay untouched. As demonstrated in Figure 3, this research enhances the image painting art style rendering network.Figure 3
Image painting art style rendering.


#### 3.4.2. Image Style Conversion Network

Figure3 depicts the picture style conversion infrastructure. ResNet contains 3 convolutional layers with 5 residual blocks, as depicted in Figure 4(a). Other than for the output layer, all nonresidual convolutional layers employ ReLU activation functions other than for the output layer. No pooling layer is employed and stride convolution is employed or microstride convolution would be used for upsampling, resulting in a massive field of vision and keeping image objects from being deformed excessively. This procedure also decreases the parameters.Figure 4
Design of an artistic painting style rendering. (a) Image conversion network structure. (b) The residual framework employed in the design.
(a)(b)It is clear that the normalizing is applied on a single picture rather than a collection of images when using instance normalization to reduce the batches to 1. Figure4(b) shows that the network enhanced by instance normalization outperforms in the testing stage of painting artistic style rendering.


#### 3.4.3. Discriminant Network

Figure5 depicts the multilayer CNN used in the discriminant network D. All of the hidden layers are activated by batch normalization and LeakyReLU. Layers 1, 4, 6, and 8 are utilized to determine the perceptual loss caused by the generated image’s contrast with the style image. If the picture is from the actual dataset (True), then the judgment network returns a likelihood of it being formed by the style conversion network; otherwise, it returns 0 (Fake).Figure 5
Painting art style rendering method withD-network design.


#### 3.4.4. Content Loss Function

The content loss function (Pi) calculates the image plane loss (L2) of the concealed produced picture Y and the actual painting style picture X using Manhattan distance as follows:(6)Pi=1NHiY−HiX,where HI represents ith hidden layer’s L2-value in D-network.Various degrees of content loss may be represented as follows depending on the provided concept:(7)LcontentX,Y=∑i=1NλiPiX,Y,where λi represents balancing factor for differentiating between the “N” hidden layers i.The produced picture and the content picture have a comparable content structure as a result of reducing the perceptual loss functionLcontent.


#### 3.4.5. Style Loss Function

Because the style loss function has been used to compensate the result picture’s style deviation, incorporating color, and texture, this article employs the style rebuilding approach, which is based on the distance between the result picture and the style picture Gram matrix.The style loss value of the feature-map of the ith layer of the D-network becomes(8)GramiY=φiY∗φiYTCi×Hi×Wiand,GiYs,Y=‖GramiY−GramiYs.This work describesGi(Ys, Y) like a collection of losses (total of losses for every layer) in ability to emphasize style reconstruction of many layers.


#### 3.4.6. Perceptual Adversarial Loss Function

As a linear function, the total perceptual loss includes a mixture of content loss with style loss that might be represented as follows:(9)LstyleYs,Y=∑i=1nGiYs,Y,where λc and λs are weight factors.The total perceptual loss score is used to enhance both the style conversion networkT and D-network. The loss functions of T and D are formulated for the produced picture Y, content picture X, and style picture Ys.(10)LT=In1−DTX+Lpercand,(11)LD=InDy−In1−DTX+m−Lprec+.Under formula (11), we assign a positive boundary value m. We reduce LT using the variables of system T, which may increase the 2nd and 3rd elements of LD at the same time, because the positive boundary value m may lead the 3rd term of LD to accomplish gradient descent. Whenever LT will be less than m, the loss function LD should compute the leftover difference by updating the discriminant system to a fresh greater extent. As a result, many contrasts between the created picture and the style picture may be continually observed and analyzed by experiencing over loss.The painting style rendering system’s generation network is depicted in Figure6.Figure 6
Painting art style rendering system’s network of generation.


#### 3.4.7. Encoder

The input picture is processed using the CNN to extract features. During 1st convolutional layer (Conv Layer-1), the quantity of filters has been set at 64. The outcome of the 1st convolutional layer gets transmitted to the 2nd convolutional layer (Conv Layer-2), which has a total of 128 filters. The 3rd convolutional layer (Conv Layer-3) uses the same link and data transfer strategy as the 1st and 2nd layers. The third convolutional layer’s quantity is limited to 256. The encoder receives a 256 × 256 picture as input as well as extracts 256 (64 × 64) feature vectors.


#### 3.4.8. Converter

The goal of the converter is to integrate many retrieved features and employ them to figure out how to translate the picture’s feature vector from the X-area (picture style model) towards the Y-area feature vector (created picture).The feature vector is converted using the 6-layer ResNet unit, as illustrated in the following equation:(12)f1=H1fl−1+fl−1,where Hl represents nonlinear transformation function, fl−1 represents first layer’s input characteristics, fl represents first layer’s style conversion output characteristics, and l represents the layer.In replacement of the usual ResNet model, the converter employs the DenseNet design. The DenseNet design may help to decrease gradient vanishing, improve feature transmission, as well as reducing the complexity. The DenseNet design links modules, allowing data flow between them to be better coupled. As demonstrated in the following equation, the DenseNet model’s lth module receives the feature mapping from the previous module.(13)f1=H1f0,f1,…,fl−1.The DenseNet unit is included into the converter in this study to decrease parameters of the model, eliminate fitting problem, and minimize computation time.


#### 3.4.9. Decoder

The decoder changes the direction as the encoder, employing a 3-level deconvolution layer (Deconv layer) to gradually recover the picture’s low-level characteristics from the feature vector till the picture is taken. Table1 provides the internals of the encoder, converter, and decoder.Table 1
Fundamental design of the painting style rendering network.
EncoderConvolutional kernelsNarrative featuresStride of operationsMethod for linking a networkFilter’s count17 ∗ 7Conv, BN, ReLU64125 ∗ 5Conv, BN, ReLU128133 ∗ 3Conv, BN, ReLU2562TransformerElements1Dense block 12Dense block 23Dense block 34Dense block 45Dense block 5DecoderConvolutional kernelsMethod of linking a networkFilter’s countSteps13 ∗ 3De-conv, BN, ReLU1281/223 ∗ 3De-conv, BN, ReLU641/237 ∗ 7De-conv, BN, ReLU31


#### 3.4.10. Discriminator

We feed the picture into the discriminator throughout this model, which determines whether it is the source image or one made by the generator. The discriminator’s picture input is separated into numerous 70 × 70 picture fragments. The system convolves the input picture layer-by-layer, distinguishes every image block via the one-dimensional output convolutional layer, and uses the mean of the judgment outcomes of each image block as the picture judgment outcome in the discriminator’s discriminating phase.


## 3.1. Dataset

A total of 7,500 traditional Chinese paints (TCPs) as well as 8,800 oil paintings (OPs) were used in the research [21]. A new class is created for every painting. There will be 156 images in every class after the image enhancement is complete. The statistics now have 1,175,000 and 1,375,000 subjects as samples.


## 3.2. Data Preprocessing

### 3.2.1. Denoising Using Median Filter

In digital imaging, MF is a nonlinear filtration that has been employed to eliminate the distortion from the dataset. MF is being used frequently because it can keep the edge when eliminating distortion under certain circumstances. Filtering a picture window-wise with MF replaces every element with the midpoint of the next closest ones. MF is a nonlinear smoothness approach that completely nullifies noise while still preserving the edge for certain noises (like random noise and salt-pepper noise). The edge of melanoma is still the most crucial aspect because most cancerous cells have a nodular shape. For an effective diagnosis, the shape of the edge contains critical data. As a result, the MF is critical in preprocessing because it preserves the edge design. Figure2 shows the functioning of MF (median filter).Figure 2
Function of median filter.


### 3.2.2. Contour-Based Image Enhancement (CIE)

CIE is an important key for outlines. By using CIE, the boundary of the skin lesion can be retrieved. The lesion portion of the digital picture is removed from the contour. Finally, the actual image and skin lesions are generated by combining the binary image of the diseased part and also the actual image. It could follow the directions of moving spatially and temporally. This approach is particularly useful in the domain of medical imaging since it aids to increase contrast, particularly whenever the ROI and surroundings have similar contrast values. The contrast augmentation index (CAI) formula is used to describe the image’s contrast as a parameter.(1)CAI=CprocssedCactual,where Cprocssed represents value of the contrast of the processed image and Cactual represents value of the contrast of the actual image.(2)Image area contrast=m−sm+s,where m represents gray-level value of the “foreground” of the image and s represents gray-level value of the “background” of the image.


## 3.2.1. Denoising Using Median Filter

In digital imaging, MF is a nonlinear filtration that has been employed to eliminate the distortion from the dataset. MF is being used frequently because it can keep the edge when eliminating distortion under certain circumstances. Filtering a picture window-wise with MF replaces every element with the midpoint of the next closest ones. MF is a nonlinear smoothness approach that completely nullifies noise while still preserving the edge for certain noises (like random noise and salt-pepper noise). The edge of melanoma is still the most crucial aspect because most cancerous cells have a nodular shape. For an effective diagnosis, the shape of the edge contains critical data. As a result, the MF is critical in preprocessing because it preserves the edge design. Figure2 shows the functioning of MF (median filter).Figure 2
Function of median filter.


## 3.2.2. Contour-Based Image Enhancement (CIE)

CIE is an important key for outlines. By using CIE, the boundary of the skin lesion can be retrieved. The lesion portion of the digital picture is removed from the contour. Finally, the actual image and skin lesions are generated by combining the binary image of the diseased part and also the actual image. It could follow the directions of moving spatially and temporally. This approach is particularly useful in the domain of medical imaging since it aids to increase contrast, particularly whenever the ROI and surroundings have similar contrast values. The contrast augmentation index (CAI) formula is used to describe the image’s contrast as a parameter.(1)CAI=CprocssedCactual,where Cprocssed represents value of the contrast of the processed image and Cactual represents value of the contrast of the actual image.(2)Image area contrast=m−sm+s,where m represents gray-level value of the “foreground” of the image and s represents gray-level value of the “background” of the image.


## 3.3. Feature Extraction Using Gabor Filter Bank (GFB)

GFB is commonly employed to retrieve characteristics in painting pictures, it could retrieve the spatial/frequency data. Amplitude, phases, and direction are the 3 kinds of characteristics created by the GFB. A sine plane wave modulates a Gaussian envelop in such filter banks. Throughout the spatial region, the Gabor filter is stated as follows:(3)Sx,y=fc2πtnefn2/t2x2+fn2/n2y2ej2πfcx′.Here,(4)x′=xcosθd+ysinθd,y′=−xsinθd+ycosθd,fc=fmax2c/2,θd=dπ8,where fc is centre frequency (fmax = ¼) and θd is direction.The proportion of the center frequency is evaluated to the size of Gaussian envelope usingt as well as n. Here, t=n=2 has been the most widely used variables.Throughout this research, we employed a bank of filters with 5 scales and 8 directions to retrieve distinct information from the painting pictures,c = 1 to 4 and d = 0 to 7.ConsiderI(x, y) become a grey-scale painting picture, and the feature extraction process is as follows:(5)Gc,dx,y=Ix,y∗Sc,dx,y,where Gc,dx,y represents complex filtering result.


## 3.4. Application of Style Rendering Model Using CNN

### 3.4.1. Image Style Rendering Procedure

The image style rendering methodology is split into 2 phases: training and executing. Transform network models are trained on each of the style mapsYs that are selected during the training stage. Each time the training set is rerun, a new set of samples is generated for the content map. Iteration after iteration, X and Y are randomly transported from the conversion style structure to the discriminant structure D. By comparing the adversarial loss functions, network D determines whether the content is X or Y and if the style is Ys or Y, and then feeds that information back to the network T. T then makes any necessary alterations to the weights and settings before moving on to the next iteration. A continual process of optimization takes place in network D in order to uncover more disparities. The ultimate goal is to create a Y style image conversion network model. Real-time conversion of any content map to a Y style-impact picture is achieved by feeding it into a model that has been well-trained. This means that the real underpinnings stay untouched. As demonstrated in Figure 3, this research enhances the image painting art style rendering network.Figure 3
Image painting art style rendering.


### 3.4.2. Image Style Conversion Network

Figure3 depicts the picture style conversion infrastructure. ResNet contains 3 convolutional layers with 5 residual blocks, as depicted in Figure 4(a). Other than for the output layer, all nonresidual convolutional layers employ ReLU activation functions other than for the output layer. No pooling layer is employed and stride convolution is employed or microstride convolution would be used for upsampling, resulting in a massive field of vision and keeping image objects from being deformed excessively. This procedure also decreases the parameters.Figure 4
Design of an artistic painting style rendering. (a) Image conversion network structure. (b) The residual framework employed in the design.
(a)(b)It is clear that the normalizing is applied on a single picture rather than a collection of images when using instance normalization to reduce the batches to 1. Figure4(b) shows that the network enhanced by instance normalization outperforms in the testing stage of painting artistic style rendering.


### 3.4.3. Discriminant Network

Figure5 depicts the multilayer CNN used in the discriminant network D. All of the hidden layers are activated by batch normalization and LeakyReLU. Layers 1, 4, 6, and 8 are utilized to determine the perceptual loss caused by the generated image’s contrast with the style image. If the picture is from the actual dataset (True), then the judgment network returns a likelihood of it being formed by the style conversion network; otherwise, it returns 0 (Fake).Figure 5
Painting art style rendering method withD-network design.


### 3.4.4. Content Loss Function

The content loss function (Pi) calculates the image plane loss (L2) of the concealed produced picture Y and the actual painting style picture X using Manhattan distance as follows:(6)Pi=1NHiY−HiX,where HI represents ith hidden layer’s L2-value in D-network.Various degrees of content loss may be represented as follows depending on the provided concept:(7)LcontentX,Y=∑i=1NλiPiX,Y,where λi represents balancing factor for differentiating between the “N” hidden layers i.The produced picture and the content picture have a comparable content structure as a result of reducing the perceptual loss functionLcontent.


### 3.4.5. Style Loss Function

Because the style loss function has been used to compensate the result picture’s style deviation, incorporating color, and texture, this article employs the style rebuilding approach, which is based on the distance between the result picture and the style picture Gram matrix.The style loss value of the feature-map of the ith layer of the D-network becomes(8)GramiY=φiY∗φiYTCi×Hi×Wiand,GiYs,Y=‖GramiY−GramiYs.This work describesGi(Ys, Y) like a collection of losses (total of losses for every layer) in ability to emphasize style reconstruction of many layers.


### 3.4.6. Perceptual Adversarial Loss Function

As a linear function, the total perceptual loss includes a mixture of content loss with style loss that might be represented as follows:(9)LstyleYs,Y=∑i=1nGiYs,Y,where λc and λs are weight factors.The total perceptual loss score is used to enhance both the style conversion networkT and D-network. The loss functions of T and D are formulated for the produced picture Y, content picture X, and style picture Ys.(10)LT=In1−DTX+Lpercand,(11)LD=InDy−In1−DTX+m−Lprec+.Under formula (11), we assign a positive boundary value m. We reduce LT using the variables of system T, which may increase the 2nd and 3rd elements of LD at the same time, because the positive boundary value m may lead the 3rd term of LD to accomplish gradient descent. Whenever LT will be less than m, the loss function LD should compute the leftover difference by updating the discriminant system to a fresh greater extent. As a result, many contrasts between the created picture and the style picture may be continually observed and analyzed by experiencing over loss.The painting style rendering system’s generation network is depicted in Figure6.Figure 6
Painting art style rendering system’s network of generation.


### 3.4.7. Encoder

The input picture is processed using the CNN to extract features. During 1st convolutional layer (Conv Layer-1), the quantity of filters has been set at 64. The outcome of the 1st convolutional layer gets transmitted to the 2nd convolutional layer (Conv Layer-2), which has a total of 128 filters. The 3rd convolutional layer (Conv Layer-3) uses the same link and data transfer strategy as the 1st and 2nd layers. The third convolutional layer’s quantity is limited to 256. The encoder receives a 256 × 256 picture as input as well as extracts 256 (64 × 64) feature vectors.


### 3.4.8. Converter

The goal of the converter is to integrate many retrieved features and employ them to figure out how to translate the picture’s feature vector from the X-area (picture style model) towards the Y-area feature vector (created picture).The feature vector is converted using the 6-layer ResNet unit, as illustrated in the following equation:(12)f1=H1fl−1+fl−1,where Hl represents nonlinear transformation function, fl−1 represents first layer’s input characteristics, fl represents first layer’s style conversion output characteristics, and l represents the layer.In replacement of the usual ResNet model, the converter employs the DenseNet design. The DenseNet design may help to decrease gradient vanishing, improve feature transmission, as well as reducing the complexity. The DenseNet design links modules, allowing data flow between them to be better coupled. As demonstrated in the following equation, the DenseNet model’s lth module receives the feature mapping from the previous module.(13)f1=H1f0,f1,…,fl−1.The DenseNet unit is included into the converter in this study to decrease parameters of the model, eliminate fitting problem, and minimize computation time.


### 3.4.9. Decoder

The decoder changes the direction as the encoder, employing a 3-level deconvolution layer (Deconv layer) to gradually recover the picture’s low-level characteristics from the feature vector till the picture is taken. Table1 provides the internals of the encoder, converter, and decoder.Table 1
Fundamental design of the painting style rendering network.
EncoderConvolutional kernelsNarrative featuresStride of operationsMethod for linking a networkFilter’s count17 ∗ 7Conv, BN, ReLU64125 ∗ 5Conv, BN, ReLU128133 ∗ 3Conv, BN, ReLU2562TransformerElements1Dense block 12Dense block 23Dense block 34Dense block 45Dense block 5DecoderConvolutional kernelsMethod of linking a networkFilter’s countSteps13 ∗ 3De-conv, BN, ReLU1281/223 ∗ 3De-conv, BN, ReLU641/237 ∗ 7De-conv, BN, ReLU31


### 3.4.10. Discriminator

We feed the picture into the discriminator throughout this model, which determines whether it is the source image or one made by the generator. The discriminator’s picture input is separated into numerous 70 × 70 picture fragments. The system convolves the input picture layer-by-layer, distinguishes every image block via the one-dimensional output convolutional layer, and uses the mean of the judgment outcomes of each image block as the picture judgment outcome in the discriminator’s discriminating phase.


## 3.4.1. Image Style Rendering Procedure

The image style rendering methodology is split into 2 phases: training and executing. Transform network models are trained on each of the style mapsYs that are selected during the training stage. Each time the training set is rerun, a new set of samples is generated for the content map. Iteration after iteration, X and Y are randomly transported from the conversion style structure to the discriminant structure D. By comparing the adversarial loss functions, network D determines whether the content is X or Y and if the style is Ys or Y, and then feeds that information back to the network T. T then makes any necessary alterations to the weights and settings before moving on to the next iteration. A continual process of optimization takes place in network D in order to uncover more disparities. The ultimate goal is to create a Y style image conversion network model. Real-time conversion of any content map to a Y style-impact picture is achieved by feeding it into a model that has been well-trained. This means that the real underpinnings stay untouched. As demonstrated in Figure 3, this research enhances the image painting art style rendering network.Figure 3
Image painting art style rendering.


## 3.4.2. Image Style Conversion Network

Figure3 depicts the picture style conversion infrastructure. ResNet contains 3 convolutional layers with 5 residual blocks, as depicted in Figure 4(a). Other than for the output layer, all nonresidual convolutional layers employ ReLU activation functions other than for the output layer. No pooling layer is employed and stride convolution is employed or microstride convolution would be used for upsampling, resulting in a massive field of vision and keeping image objects from being deformed excessively. This procedure also decreases the parameters.Figure 4
Design of an artistic painting style rendering. (a) Image conversion network structure. (b) The residual framework employed in the design.
(a)(b)It is clear that the normalizing is applied on a single picture rather than a collection of images when using instance normalization to reduce the batches to 1. Figure4(b) shows that the network enhanced by instance normalization outperforms in the testing stage of painting artistic style rendering.


## 3.4.3. Discriminant Network

Figure5 depicts the multilayer CNN used in the discriminant network D. All of the hidden layers are activated by batch normalization and LeakyReLU. Layers 1, 4, 6, and 8 are utilized to determine the perceptual loss caused by the generated image’s contrast with the style image. If the picture is from the actual dataset (True), then the judgment network returns a likelihood of it being formed by the style conversion network; otherwise, it returns 0 (Fake).Figure 5
Painting art style rendering method withD-network design.


## 3.4.4. Content Loss Function

The content loss function (Pi) calculates the image plane loss (L2) of the concealed produced picture Y and the actual painting style picture X using Manhattan distance as follows:(6)Pi=1NHiY−HiX,where HI represents ith hidden layer’s L2-value in D-network.Various degrees of content loss may be represented as follows depending on the provided concept:(7)LcontentX,Y=∑i=1NλiPiX,Y,where λi represents balancing factor for differentiating between the “N” hidden layers i.The produced picture and the content picture have a comparable content structure as a result of reducing the perceptual loss functionLcontent.


## 3.4.5. Style Loss Function

Because the style loss function has been used to compensate the result picture’s style deviation, incorporating color, and texture, this article employs the style rebuilding approach, which is based on the distance between the result picture and the style picture Gram matrix.The style loss value of the feature-map of the ith layer of the D-network becomes(8)GramiY=φiY∗φiYTCi×Hi×Wiand,GiYs,Y=‖GramiY−GramiYs.This work describesGi(Ys, Y) like a collection of losses (total of losses for every layer) in ability to emphasize style reconstruction of many layers.


## 3.4.6. Perceptual Adversarial Loss Function

As a linear function, the total perceptual loss includes a mixture of content loss with style loss that might be represented as follows:(9)LstyleYs,Y=∑i=1nGiYs,Y,where λc and λs are weight factors.The total perceptual loss score is used to enhance both the style conversion networkT and D-network. The loss functions of T and D are formulated for the produced picture Y, content picture X, and style picture Ys.(10)LT=In1−DTX+Lpercand,(11)LD=InDy−In1−DTX+m−Lprec+.Under formula (11), we assign a positive boundary value m. We reduce LT using the variables of system T, which may increase the 2nd and 3rd elements of LD at the same time, because the positive boundary value m may lead the 3rd term of LD to accomplish gradient descent. Whenever LT will be less than m, the loss function LD should compute the leftover difference by updating the discriminant system to a fresh greater extent. As a result, many contrasts between the created picture and the style picture may be continually observed and analyzed by experiencing over loss.The painting style rendering system’s generation network is depicted in Figure6.Figure 6
Painting art style rendering system’s network of generation.


## 3.4.7. Encoder

The input picture is processed using the CNN to extract features. During 1st convolutional layer (Conv Layer-1), the quantity of filters has been set at 64. The outcome of the 1st convolutional layer gets transmitted to the 2nd convolutional layer (Conv Layer-2), which has a total of 128 filters. The 3rd convolutional layer (Conv Layer-3) uses the same link and data transfer strategy as the 1st and 2nd layers. The third convolutional layer’s quantity is limited to 256. The encoder receives a 256 × 256 picture as input as well as extracts 256 (64 × 64) feature vectors.


## 3.4.8. Converter

The goal of the converter is to integrate many retrieved features and employ them to figure out how to translate the picture’s feature vector from the X-area (picture style model) towards the Y-area feature vector (created picture).The feature vector is converted using the 6-layer ResNet unit, as illustrated in the following equation:(12)f1=H1fl−1+fl−1,where Hl represents nonlinear transformation function, fl−1 represents first layer’s input characteristics, fl represents first layer’s style conversion output characteristics, and l represents the layer.In replacement of the usual ResNet model, the converter employs the DenseNet design. The DenseNet design may help to decrease gradient vanishing, improve feature transmission, as well as reducing the complexity. The DenseNet design links modules, allowing data flow between them to be better coupled. As demonstrated in the following equation, the DenseNet model’s lth module receives the feature mapping from the previous module.(13)f1=H1f0,f1,…,fl−1.The DenseNet unit is included into the converter in this study to decrease parameters of the model, eliminate fitting problem, and minimize computation time.


## 3.4.9. Decoder

The decoder changes the direction as the encoder, employing a 3-level deconvolution layer (Deconv layer) to gradually recover the picture’s low-level characteristics from the feature vector till the picture is taken. Table1 provides the internals of the encoder, converter, and decoder.Table 1
Fundamental design of the painting style rendering network.
EncoderConvolutional kernelsNarrative featuresStride of operationsMethod for linking a networkFilter’s count17 ∗ 7Conv, BN, ReLU64125 ∗ 5Conv, BN, ReLU128133 ∗ 3Conv, BN, ReLU2562TransformerElements1Dense block 12Dense block 23Dense block 34Dense block 45Dense block 5DecoderConvolutional kernelsMethod of linking a networkFilter’s countSteps13 ∗ 3De-conv, BN, ReLU1281/223 ∗ 3De-conv, BN, ReLU641/237 ∗ 7De-conv, BN, ReLU31


## 3.4.10. Discriminator

We feed the picture into the discriminator throughout this model, which determines whether it is the source image or one made by the generator. The discriminator’s picture input is separated into numerous 70 × 70 picture fragments. The system convolves the input picture layer-by-layer, distinguishes every image block via the one-dimensional output convolutional layer, and uses the mean of the judgment outcomes of each image block as the picture judgment outcome in the discriminator’s discriminating phase.


## 4. Performance Analysis

This paper’s experiment was conducted in a setting as follows: 64 bit operating system with 3.20 GHz Intel Core i7-9700K processor. Pytorch 1.10 is a deep learning platform for Windows 10. Regarding CNN implementation, this open-source platform is mostly developed in Python and may be employed on any environment. About 70% of the data generated is employed for training, 15% for verification, and 15% for test. Here, the performance metrics of the proposed technique like accuracy, information loss, average value of loss function, and time consumption are examined and are compared with the existing techniques.


### 4.1. Accuracy

The percentage of pixels inside the picture that were properly identified may be used as an additional statistic for evaluating segmentation. Including individual classes, the pixel accuracy is often typically stated as a whole throughout all categories. The accuracy of existing and proposed approaches is depicted in Figure7.Figure 7
Comparison of accuracy of existing and proposed techniques.


### 4.2. Rate of Information Loss

Information loss is also used to signify inability to retrieve all information accessible in statistical research regarding a specific theme. The rate of information loss of existing and proposed approaches is depicted in Figure8.Figure 8
Comparison of rate of information loss of existing and proposed techniques.


### 4.3. Time Consumption

The total time that people spent using a particular volume of work is referred to as time consumption. The time consumption of existing and proposed approaches is depicted in Figure9.Figure 9
Comparison of time consumption of existing and proposed techniques.


### 4.4. Average Value of Loss Function

A quantitative model’s loss function establishes a consequence for making an inaccurate prediction. An estimate’s accuracy is typically measured by how much it differs from the actual number, but it can also be specified in terms of a simple binary value based on whether or not the forecast seems valid within a specified range. The average value of loss function of existing and proposed approaches is depicted in Figure10.Figure 10
Comparison of average value of loss function of existing and proposed techniques.


### 4.5. Discussion

In texture image compression algorithm (existing), texture mapping is a method that has been suggested to resolve the discrepancy between actual time and the reality that exists. Since its inception, it has been the subject of much research and application. As a result of the restricted bandwidth and memory storage available, it presents difficulties in stain dyeing a large number of huge texture pictures; as a result, texture compression is used. Not only texture compression may increase cache use rates but it can also significantly lessen the strain on data transfer created by the system, which in turn cause to address the issue of real-time rendering of realistic visuals to a substantial extent. Given the uniqueness of texture image compression, it is vital to evaluate not only the quality of the texture image after compression ratio and decompression, but also if the technique is compatible with unpopular graphics cards as well as the compression ratio [22]. In P2 GAN (existing) method nonconvergence occurs when the model parameters fluctuate, destabilize, and never reach a state of convergence. Mode collapse occurs when the generator collapses, resulting in a restricted number of sample variations. The gradient is reduced when the discriminator is too successful, the generating gradient disappears, and the discriminator learns absolutely nothing [23]. In Chip GAN (existing), realistic western paintings have been created using style transfer techniques applied to photographs. However, since the painting techniques used in Chinese and western paintings are fundamentally different, simply using current methods to the transfer of Chinese ink-wash painting style would not provide adequate results [24]. In Cycle GAN (existing) method each input picture is associated with a single output image, which is a fundamental disadvantage of CycleGAN. We think most cross-domain connections are complicated and should be described as many-to-many. With these problems in mind, we have developed the U-CNN for style rendering model enhancement.


## 4.1. Accuracy

The percentage of pixels inside the picture that were properly identified may be used as an additional statistic for evaluating segmentation. Including individual classes, the pixel accuracy is often typically stated as a whole throughout all categories. The accuracy of existing and proposed approaches is depicted in Figure7.Figure 7
Comparison of accuracy of existing and proposed techniques.


## 4.2. Rate of Information Loss

Information loss is also used to signify inability to retrieve all information accessible in statistical research regarding a specific theme. The rate of information loss of existing and proposed approaches is depicted in Figure8.Figure 8
Comparison of rate of information loss of existing and proposed techniques.


## 4.3. Time Consumption

The total time that people spent using a particular volume of work is referred to as time consumption. The time consumption of existing and proposed approaches is depicted in Figure9.Figure 9
Comparison of time consumption of existing and proposed techniques.


## 4.4. Average Value of Loss Function

A quantitative model’s loss function establishes a consequence for making an inaccurate prediction. An estimate’s accuracy is typically measured by how much it differs from the actual number, but it can also be specified in terms of a simple binary value based on whether or not the forecast seems valid within a specified range. The average value of loss function of existing and proposed approaches is depicted in Figure10.Figure 10
Comparison of average value of loss function of existing and proposed techniques.


## 4.5. Discussion

In texture image compression algorithm (existing), texture mapping is a method that has been suggested to resolve the discrepancy between actual time and the reality that exists. Since its inception, it has been the subject of much research and application. As a result of the restricted bandwidth and memory storage available, it presents difficulties in stain dyeing a large number of huge texture pictures; as a result, texture compression is used. Not only texture compression may increase cache use rates but it can also significantly lessen the strain on data transfer created by the system, which in turn cause to address the issue of real-time rendering of realistic visuals to a substantial extent. Given the uniqueness of texture image compression, it is vital to evaluate not only the quality of the texture image after compression ratio and decompression, but also if the technique is compatible with unpopular graphics cards as well as the compression ratio [22]. In P2 GAN (existing) method nonconvergence occurs when the model parameters fluctuate, destabilize, and never reach a state of convergence. Mode collapse occurs when the generator collapses, resulting in a restricted number of sample variations. The gradient is reduced when the discriminator is too successful, the generating gradient disappears, and the discriminator learns absolutely nothing [23]. In Chip GAN (existing), realistic western paintings have been created using style transfer techniques applied to photographs. However, since the painting techniques used in Chinese and western paintings are fundamentally different, simply using current methods to the transfer of Chinese ink-wash painting style would not provide adequate results [24]. In Cycle GAN (existing) method each input picture is associated with a single output image, which is a fundamental disadvantage of CycleGAN. We think most cross-domain connections are complicated and should be described as many-to-many. With these problems in mind, we have developed the U-CNN for style rendering model enhancement.


## 5. Conclusion

Modern image processing tools have posed new challenges to oil painting as a key source of data, having a profound impact on artistic creativity. This article presented a novel U-CNN approach to boost the style rendering model through tackling the issues. For this investigation, TCPs and OPs were taken as datasets and were provided into the preprocessing stage to normalize the raw datasets by utilizing the median filter and CIE approach. To extract the features of the normalized data, the GFB was used. The proposed approach was employed for the improvement of rendering model. Furthermore, the performance of the proposed technique was matched with the existing techniques in terms of accuracy, information loss, average value of loss function, and time consumption. Finally, our proposed approach accomplished the greatest effectiveness over the existing approaches regarding oil painting style rendering model.



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*Source: 9590995-2022-07-08.xml*