Image matting refers to extracting precise alpha matte from natural images, and it plays a critical role in various downstream applications, such as image editing. Despite being an ill-posed problem, traditional methods have been trying to solve it for decades. The emergence of deep learning has revolutionized the field of image matting and given birth to multiple new techniques, including automatic, interactive, and referring image matting. This paper presents a comprehensive review of recent advancements in image matting in the era of deep learning. We focus on two fundamental sub-tasks: auxiliary input-based image matting, which involves user-defined input to predict the alpha matte, and automatic image matting, which generates results without any manual intervention. We systematically review the existing methods for these two tasks according to their task settings and network structures and provide a summary of their advantages and disadvantages. Furthermore, we introduce the commonly used image matting datasets and evaluate the performance of representative matting methods both quantitatively and qualitatively. Finally, we discuss relevant applications of image matting and highlight existing challenges and potential opportunities for future research. We also maintain a public repository to track the rapid development of deep image matting at https://github.com/JizhiziLi/matting-survey.

翻译：图像抠图是指从自然图像中提取精确的alpha遮罩，在图像编辑等多种下游应用中发挥着关键作用。尽管这是一个病态问题，传统方法已尝试解决数十年。深度学习的出现彻底改变了图像抠图领域，催生了多种新技术，包括自动抠图、交互式抠图和参照抠图。本文全面回顾了深度学习时代图像抠图的最新进展。我们聚焦于两个基本子任务：基于辅助输入的图像抠图（涉及用户定义输入以预测alpha遮罩）和自动图像抠图（无需任何人工干预即可生成结果）。我们根据任务设定和网络结构系统梳理了这两类任务的现有方法，并总结了其优缺点。此外，我们介绍了常用的图像抠图数据集，并从定量和定性两方面评估了代表性抠图方法的性能。最后，我们讨论了图像抠图的相关应用，并指出了当前面临的挑战及未来研究的潜在机遇。我们还维护了一个公开仓库（https://github.com/JizhiziLi/matting-survey）以追踪深度图像抠图的快速发展。