# _Applied sciences_
## 4 Experimental Evaluation
### Comparison Experiment
We have found salt-and-pepper noise in the images that were output by U-Net. Combining the data in Figure 3 and Table 6, it can be seen that although all three painted images belong to Dunhuang murals, there are significant differences in shape and color between the training samples and the test samples. In this context, the proposed UR-Net model can still recognize the earth red and ultramarine blue pixels more accurately, which  
Figure 17: Differences between the true results and segmentation results for different segmentation methods. (**a**) K-means clustering. (**b**) ResNet. (**c**) U-Net. (**d**) The proposed UR-Net. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Dataset** & **TP** & **TN** & **FN** & **FP** & **TP + TN** & **FN + FP** \\ \hline \multirow{2}{*}{K-means} & Earth red & 42,135 & 292,095 & 4411 & 659 & 334,230 & 5070 \\  & Ultramarine blue & 32,638 & 290,624 & 15,795 & 243 & 323,262 & 16,038 \\ \hline \multirow{2}{*}{ResNet} & Earth red & 45,122 & 267,552 & 1424 & 25,202 & 312,674 & 26,626 \\  & Ultramarine blue & 41,922 & 283,193 & 6511 & 7674 & 325,115 & 14,185 \\ \hline \multirow{2}{*}{U-Net} & Earth red & 45,180 & 289,657 & 1366 & 3097 & 334,837 & 4463 \\  & Ultramarine blue & 47,480 & 287,229 & 953 & 3638 & 334,709 & 4591 \\ \hline \multirow{2}{*}{UR-Net} & Earth red & 45,703 & 290,399 & 843 & 2355 & 336,102 & 3198 \\  & Ultramarine blue & 46,833 & 288,719 & 1600 & 2148 & 335,552 & 3748 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: TP + TN and FN + FP calculated based on TP, TN, FN, and FP. proves the high application value of UR-Net in the color recognition and segmentation of color paintings. Combining the experimental data in Tables 3 and 4 and Figure 17, it can be seen that the proposed UR-Net model has a higher accuracy than the other two methods for segmenting the painting dataset and is more suitable for complex painted images. The segmentation results of the ultramarine blue pixels in Figure 17a show large magenta areas, indicating that the K-means clustering algorithm overfitted when performing full-cluster segmentation on the input image, overidentifying the background pixels and struggling to achieve color consistency. A good result is obtained when segmenting the earth red pixels, but the data values, including IoU, are still inferior to those of the proposed method, and the required accuracy is not achieved. The comparison images of earth red and ultramarine blue pixels in Figure 17b both show large green and magenta areas, and the data values, including IoU, are widely different from those expected, indicating that ResNet displays insufficient segmentation accuracy in achieving color consistency, which is mainly due to the insufficient amount of data learned by ResNet due to the small number of training samples. The green and magenta areas in Figure 17c are significantly reduced, and the data values such as IoU are also significantly improved, indicating that U-Net can still achieve better color consistency when performing segmentation tasks with insufficient training samples; however, the output image contains salt-and-pepper noise, which affects the segmentation accuracy. To address the above issues, the proposed UR-Net adds improved residual blocks to the U-Net architecture, relying on the powerful feature extraction ability of U-Net and the function of residual blocks to reduce information loss, which allows for more accurate training using a small number of samples. On the other hand, UR-Net displays some uncertainty during the training process and needs to be trained several times to obtain satisfactory results; therefore, we aim to improve this in future work.