# Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds
## 5 Conclusions  
This paper addresses the problem of portrait image matting for complex backgrounds, designs a method that combines background restoration and detail matting, and verifies the method's effectiveness through experiments. This paper proposes a method, MORLIPO, for portrait image matting of complex backgrounds, which is based on the MobileNetV2 lightweight model. We further construct the background restoration module BRM and add the detail matting module FGMatting to realize the matting of complex backgrounds and details to obtain better matting results. This paper uses a quantitative measure to assess the effectiveness of the proposed method. It conducts experiments on various real image video data sets and compares the performance of MORLIPO with other matting algorithms and previous data. The quantitative measure includes objective metrics such as MAD, MSE, Grad, and Conn indicators to evaluate the quality and stability of the image and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & **MAD** & **MSE** & **Grad** & **Conn** \\ \hline BGMv2 & MobileNetV2 & 31.80 & 30.32 & 32.40 & 5.31 \\ MODNet & MobileNetV2 & 24.04 & 15.53 & 38.88 & 4.28 \\ RVM & MobileNetV3 & 27.50 & 21.31 & 34.18 & 2.12 \\ PP-Matting & MobileNetV3 & 20.03 & 14.32 & 33.45 & 0.49 \\ MORLIPO & MobileNetV2 & **20.01** & **13.49** & **33.40** & **0.47** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Objective evaluation metrics Equations (6)–(9) of the four methods for video matting on the same data set (bold indicates the best-ranked value). Figure 4: Comparison of experiments on the video (the red dashed box indicates better results compared to other methods). video matting results. Experimental results show that MORLIPO can significantly improve the quality and stability of the image and video matting. The method can be applied to image processing in a wide variety of scenarios involving image editing [39], video conferencing [6], medical imaging [40], cloud detection [41], game production, intelligent transportation [42], and, more recently, multimodal 3D applications [43]. However, our method has limitations for multimodal image editing tasks, and the currently realized multimodal portrait matting is not ideal. For future work, we plan to apply more advanced multimodal image matting techniques to address the above challenges. First, we will work on creating more efficient data sets for image matting that utilize the powerful learning capabilities of deep learning to obtain better matting results. Second, more complex and powerful feature fusion modules will be added to improve the model's performance. Third, multimodal matting can be further investigated to try to implement portrait matting in conjunction with other modalities such as text, speech, and eye gaze. Hopefully, this study will provide a good reference for researchers studying image matting and give some information about this rapidly developing and important field. Conceptualization, R.L. and D.Z. ; formal analysis, R.L. ; methodology, R.L. and D.Z. ; validation, R.L. ; writing--original draft, R.L. ; writing--review and editing, D.Z., M.-Q.Z. and S.-L.G. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the National Nature Science Foundation of China (No. 62102213, 62262056); Independent project fund of State Key lab of Tibetan Intelligent Information Processing and Application (Coestablished by province and ministry): 2022 SKL014; Qinghai Province Key R&D and Transformation Programme (No. 2022-QY-203); National Key R&D plan (No. 2020 YFC1523305).