# Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds
## 4 Experiments
### Analysis of Experimental Results  
To better study the different image matting methods, we conducted quantitative experiments to verify their effectiveness. From each data set, a pair of images analyzed more frequently is selected separately, and four different methods are chosen for comparison. The experimental results are shown below. Table 1 lists the objective performance of other matting methods on the same data set. The value indicating the best performance for each metric is shown in bold. Figure 3 shows the experimental results of the selected methods on the same data set.  
As can be seen in Figure 3, our method performs well in different matting situations, such as hair, holes, large movements, and complex backgrounds. In addition, our method has a clear advantage when dealing with images with complex backgrounds and large movements. Previous methods can only deal with this situation better when there is auxiliary input, such as BGMv2. Still, our method does not need to input an additional background image and can realize fine matting using the background restoration method.  
In the video matting, the background is relatively simple, the portrait movement is slight, and several methods are somewhat effective, as shown in Figure 4 in the first and second rows. When the portrait background is relatively complex, multiple target characters appear, as shown in the third row. The background changes quickly, as shown in the fourth row. Our method and the BGMv2 method are visually better. Moreover, our method is easier to apply in real environments by the method of background restoration, which doesn't require additional input.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & **MAD** & **MSE** & **Grad** & **Conn** \\ \hline BGMv2 & MobileNetV2 & 33.90 & 28.39 & 2.38 & 4.52 \\ MODNet & MobileNetV2 & 7.36 & 2.6 & 1.58 & 0.60 \\ RVM & MobileNetV3 & 6.36 & 1.47 & 1.03 & 0.45 \\ PP-Matting & MobileNetV3 & 5.91 & 1.21 & 0.76 & 0.39 \\ MORLIPO & MobileNetV2 & **5.81** & **1.20** & **0.50** & **0.39** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Objective evaluation metrics Equations (6)–(9) of the four methods for image matting on the same data set (bold indicates the best-ranked value).  
Figure 3: Comparison of experiments on the image (the red dashed box indicates better results compared to other methods).  
The quantitative evaluation results are shown in Tables 1 and 2, measured by MAD, MSE, Grad, and Conn. Table 1 shows the comparison of the MORLIPO method with the current matting methods for people images. MORLIPO is better than BGMv2, MODNet, RVM, and PP-Matting on all metrics except SAD. Table 2 shows the comparison of the MORLIPO method with the other methods regarding the character matting of the video, and all the indexes show that our method is significantly better than the other methods. All variants of our method give better results than the other methods. The main reason remains that our deep model can understand the complex background of the image and feed the recovered background as an input into the model to help achieve foreground prediction, whereas the other methods cannot. We use MobileNetV2, a model for lightweight mobile devices that is easy to use, has relatively few parameters, and is more efficient. The validation shows that our method performs well on both visual effects and objective evaluation metrics.