- Deep image matting,
- computer vision,
- deep learning,
- fusion techniques,
- U-net
Copyright (c) 2025 Liqaa M. Shoohi, Jamila H. Saud

This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k dataset demonstrate superior performance compared to traditional methods, achieving higher accuracy, faster processing speed, and improved boundary preservation. Novelty: The proposed model effectively combines deep learning with fusion techniques, enhancing matting quality while maintaining robustness across various environmental conditions. Implications: These findings highlight the potential of integrating fusion techniques with deep learning for image matting, offering valuable insights for future research in automated image processing applications, including augmented reality, gaming, and interactive video technologies.
Highlights:
- Better Precision: Fusion techniques enhance fine detail preservation.
- Faster Processing: Lightweight U-Net improves speed and accuracy.
- Wide Applications: Useful for AR, gaming, and video processing.
Keywords: Deep image matting, computer vision, deep learning, fusion techniques, U-Net
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- J. Li, J. Zhang, and D. Tao, "Deep image matting: A comprehensive survey," arXiv preprint arXiv:2304.04672, 2023.
- H. Yu, N. Xu, Z. Huang, Y. Zhou, and H. Shi, "High-resolution deep image matting," in Proc. AAAI Conf. Artif. Intell., 2021.
- S. Iqbal, A. N. Qureshi, and M. Alhussein, "Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification," Frontiers in AI, 2024.
- Y. Liang, Q. Fu, Z. Kun, and G. Liu, "Enhancing transparent object matting using predicted definite foreground and background," IEEE Trans. Image Process., 2024.
- J. Yao, X. Wang, L. Ye, and W. Liu, "Matte anything: Interactive natural image matting with segment anything model," Image Vis. Comput., 2024.
- S. Lutz and A. Smolic, "Foreground color prediction through inverse compositing," in Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis., 2021.
- M. H. Mahmood, L. T. Lim, and D. A. Awang Mat, "Deep learning in the grading of diabetic retinopathy: A review," IET Comput. Vis., 2022.
- L. Wang et al., "Medical matting: Medical image segmentation with uncertainty from the matting perspective," Comput. Biol. Med., Elsevier, 2023.
- A. Saber, M. Sakr, O. M. Abo-Seida, and A. Keshk, "A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique," IEEE Access, vol. 9, pp. 1-14, 2021.
- S. Lin, L. Yang, and I. Saleemi, "Robust high-resolution video matting with temporal guidance," in Proc. IEEE Int. Conf. Comput. Vis., 2022.
- A. S. Qaddoori, J. H. Saud, and F. A. Hamad, "A classifier design for micro bubble generators based on deep learning technique," 5 Nano, Exp. Concern, Part 5, Proc., vol. 80, no. 3, p. 1705, 2023.
- S. Liqaa M. and J. H. Saud, "Deep video understanding based on language generation," Int. J. Cloud Comput. Database Manage., vol. 6, no. 1, pp. 9-15, 2025.
- M. Talib and J. H. Saud, "A multi-weapon detection using deep learning," Iraq J. Inf. Commun. Technol. (IJICT), vol. 7, no. 1, Apr. 2024.
- X. Zhang, H. He, and J. X. Zhang, "Multi-focus image fusion based on fractional order differentiation and closed image matting," ISA Trans., 2022.
- J. Wang, X. Jiang, Y. Zhang, J. Yin, and Z. Yang, "TG Matting: Automatic image matting based on trimap generation," J. Comput. Sci., 2024.
- S. A. Tuama, J. H. Saud, and Z. A. Alobaidy, "Using an accurate multimodal biometric for human identification system via deep learning," Al-Mansour J., Special Issue HICNAS2022.
- B. Mauer and J. Venecek, "Writing the literature review," in Strategies for Conducting Literary Research, 2022.
- S. Kazi, "Fruit grading, disease detection, and an image processing strategy," J. Image Process. Artif. Intell., 2023.
- P. C. Su and M. T. Yang, "Integrating depth-based and deep learning techniques for real-time video matting without green screens," Electronics, 2024.
- Y. Liu, J. Xie, X. Shi, Y. Qiao, and Y. Huang, "Tripartite information mining and integration for image matting," in Proc. IEEE Int. Conf. Comput. Vis., 2021.
- H. Gu, H. Dong, N. Konz, and M. A. Mazurowski, "A systematic study of the foreground-background imbalance problem in deep learning for object detection," arXiv preprint arXiv:2306.16539, 2023.
- N. Xu, B. Price, S. Cohen, and T. Huang, "Deep image matting," arXiv preprint arXiv:1703.03872v3, 2017.