# Coarse-to-Fine Structure-Aware Artistic Style Transfer
## 4 Experimental Results and Analysis
### Qualitative Comparisons with Methods in Prior Works  
Inspired by the recent WCT [6] and STROTSS [2] methods, our method adopts the whiting and coloring transformation proposed in WCT and the rEMD loss proposed in STROTSS. In Figure 6, we compare our method with WCT and STROTSS. WCT can transfer the color distribution and simple texture of arbitrary style images; however, some context local structure is discarded, resulting in messy and disordered stylized images (e.g., rows 1, 2, and 3). STROTSS is an image-optimization style transfer method that transfers the visual attributes from the style image to the content image with minimum semantic distortion. Nevertheless, too many structural details are preserved, and the overall palette of the style image is not accurately transferred (e.g., rows 2 and 3). In contrast to these two methods, our method can transfer the main structure and discard some trivial details of the content image. Moreover, some notable local structures of the style image, such as brushstrokes, can be fused into the global structure of the content image, and the overall palette of the stylized image remains the same as that of the style image. For example, in the second and fourth rows, the color blocks of mountains and the brushstrokes of vegetation in our stylized images are explicitly similar to those in the style images. Our model can learn some key style structures while ignoring some unimportant content details.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Designation** & **Information** \\ \hline Operating system & Windows 10 \\ \hline System configuration & CPU: AMD Ryzen 9 5900X \\ \hline GPU: NVIDIA GeForce RTX 3090 \\ \hline Software & PyCharm 2021.3.1 (Community Edition) \\ \hline Python 3.8.12 \\ \hline Python library & Cuda 11.7 \\ \hline \multicolumn{2}{c}{Pytorch 1.8} \\ \hline Torchvision 0.9 \\ \hline \multicolumn{2}{c}{Numpy 1.21} \\ \multicolumn{2}{c}{Matplotlib 3.5.1} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental environment configuration.  
As shown in Figure 7, we compare our method with other state-of-the-art style transfer methods. [PERSON] et al. [1] proposed the original optimization-based style transfer algorithm, which can transfer the overall style texture and the color distribution. However, some incongruuous textures appear in the stylized images, leading to the stylizations' looking unnatural (e.g., rows 4, 5, and 6). Similar to our method, the method proposed by [PERSON] et al. [3] is also a feed-forward method. It can combine the local color and texture of style images with the structure of the content but often maintains too many content structures and may play a role in shifting the color histogram only in some cases (e.g., rows 1, 2, and 3). AdaIN [5] and SANet [22] are both arbitrary style transfer models, which mainly transfer simple style patterns. AdaIN often fails to transfer the color distribution of style images, and SANet has the severe problem of messy texture and disordered structure (e.g., rows 4, 5, and 6). All of the methods mentioned above maintain some unnecessary small local structures of the content images, and the essential local structures of style images are not integrated into the target image. In contrast to these methods, our model can simultaneously transfer the style color distribution accurately and combine the local style structure with the global content structure. For example, in the fourth row, the image of the rabbits generated by our method looks more harmonious and natural in the stylized image.  
Figure 6: Qualitative comparisons between our method, WCT [6] and STROTS [2]: (**a**) The content images; (**b**) The style images; (**c**) The stylized images are generated by our method; (**d**) The stylized images are generated by WCT; (**e**) The stylized images are generated by STROTS.  
It seems as though the style image consists of ink dots; the same artistic expression can be exhibited by our method.