## 4 Experiment and Discussion
### Qualitative Evaluation  
In this section, DE-GAN is compared with two state-of-the-art GAN methods, StyleGAN [12] and CycleGAN [7], using unpaired data. Figures 9 and 10 show the qualitative comparison effect diagrams of DE-GAN and the two compared methods. The same real photo was used as the input test image, and two types of artworks, sketch (Figure 9) and oil painting (Figure 10), were used as style images, respectively. It can be seen that all three methods successfully generated artistic images that were consistent with the style images. In the sketch style pictures in Figure 9, the reconstructed sketch renderings of different algorithms are subjectively visual. The case image reconstructed using DE-GAN has a clearer visual effect, and the transition between the main content of the picture and the background is more natural and clearer. The relationship between light and shade is more harmonious, and the main content is more three-dimensional. In contrast, the image background obtained by applying the StyleGAN and CycleGAN algorithms is slightly messy, which does not conform to the characteristics of real images. In the case image obtained by applying StyleGAN, it can be clearly seen that the overall image is blurred; the main content of the image is not prominent enough; and the main body and the background are integrated. It can be seen that the sketch style images reconstructed using the algorithm in this paper have relatively better contour edge information, and the lines contain more detailed information, which can better highlight the main content of the images and present a more realistic sense of brush strokes. Novice painters can learn the painting techniques of artists by examining the subtle differences between the original photo and our generated painting.  
From the style transfer of the oil painting style shown in Figure 10, it is obvious that DE-GAN, StyleGAN, and CycleGAN could all effectively transfer the oil painting style; however, it can be clearly seen that because StyleGAN and CycleGAN only produce simple image styles, the obtained artistic images have the problems of gradation blur, image distortion, and so on, and local areas are easily over-stylized. The resulting image loses the content of the original photograph. Relatively speaking, the artistic images obtained using our method have better performance. In this method, the feature information style of the four elements is extracted, and the relevant features of the content image are matched, so that the sense of hierarchy in the content image is well preserved, while the artifacts are eliminated; thus, style images with clear structure and fine details can be obtained.  
From the style transformation of the traditional Chinese ink painting style shown in Figure 11, it can be seen that although all three methods achieved style transfer, the migrated images obtained using DE-GAN and StyleGAN show contrast between the light and shade of the main body of the image and retain most of the details of the main body. This makes the generated image look clearer and more three-dimensional, which is where CycleGAN falls short. Compared with DE-GAN, the overall color obtained with the StyleGAN algorithm is redder. However, it should be noted that with the traditional Chinese ink painting style, the three methods cannot deal with the image background details and cannot keep the details of the original image background.  
Figure 11: Comparison of subjective visual effects of ink paintings.  
Figure 10: Comparison of subjective visual effects of oil paintings.