# An Improved Detail-Enhancement CycleGAN Using AdaLIN for Facial Style Transfer
## 4 Experiment
### Results
#### 4.3.1 Subjective Evaluation  
Subjective evaluation involves visually assessing an image and assigning a subjective quality rating based on personal perception. This method relies on statistical analysis and requires multiple observers for reliable results. While subjective evaluation aligns with common human perceptions and is applicable to various image types, it is labor-intensive, costly, and susceptible to human biases, leading to potential result variations. Typically, it is suitable for evaluating a small number of images, while objective methods are preferred for larger datasets. Many mainstream algorithms, as seen in references [36; 37; 38; 39], employ absolute evaluation to assess generated images. Absolute evaluation compares the generated image against a reference (real source image), using methods like Double Stimulus Continuous Scale (DSCQS). In DSCQS, observers alternate between the source and evaluated images, noting differences and scoring based on predefined criteria, such as the full superiority scale shown in Table 2. The Mean Opinion Score (MOS) is then calculated to compare the performance of different methods. The experimental results of this paper were compared with both traditional CycleGAN algorithms and excellent methods that have emerged in the past five years. The results are shown in Figure 7. When performing style transfer on general facial images (such as the first and second columns), the method proposed in this paper achieves very natural color transitions and preserves the original texture information without producing uneven color patches (for example, rows 2, 4, 6 in the first column, and rows 2, 3, 4, 5 in the second column), and does not ignore facial details (for example, row 3 in the first column). When processing style transfer for facial images with more prominent highlights (such as the third and fourth columns), our method can preserve highlight details while not losing other facial details, which other algorithms fail to achieve. When processing facial images that may produce artifacts during style transfer (such as the fifth and sixth columns), the method proposed in this paper can eliminate artifact phenomena at the edges of portraits caused by lighting conditions and the transfer process, while fully retaining the edge information of the portraits. Other methods are notably inferior in this aspect. The above comparative experiments demonstrate the advanced nature of this paper in addressing portrait style transfer problems. To evaluate visual quality based on MOS criteria (refer to Table 2), 30 individuals with diverse professional backgrounds were chosen, assessing 50 generated images from both traditional CycleGAN and the proposed method. Image quality evaluation focuses on detecting color shifts, texture distortions, high-frequency noise, or other defects. During testing, subjects viewed and rated images from both methods, and mean and standard deviation scores were calculated for comparison (see Table 3). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Score**} & **Quality** & **Description** \\  & **Standard** & \\ \hline
5 & Excellent & There is no way to tell if the image is real or fake. \\
4 & Good & Slightly perceptible but does not affect overall appearance. \\
3 & Medium & Image quality has a slight impact on the overall look and feel. \\
2 & Rather poor & Image quality has affects the overall look and feel. \\
1 & Poor quality & Image quality seriously affects the overall look and feel. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MOS scoring system.