数字图像合成一直是图像处理中的研究热点,在图片编辑,平面设计,电影特效等领域有着广泛的应用。从原图像中准确地提取目标物体并将其无缝地合成到新背景下是图像合成的基本目标。按所使用的关键技术分类,现有的数字图像合成技术可分为基于α分量的图像合成,基于梯度场的图像合成和基于多分辨率模型的图像合成。首先详述了3类方法中的典型算法,并从合成质量、鲁棒性、运算效率等方面进行分析比较,然后对新的图像合成应用方式进行了扩展介绍,最后总结了现有图像合成方法的普遍局限性,并探讨了图像合成今后面临的挑战和发展方向。

Digital image compositing is receiving increasing attention from the image processing research community, and it has been used in many applications such as photo editing, graphic design, and for visual effects in movies. The essential of image compositing is how to extract foreground object from a given image and composite it to a new background seamlessly. In this paper, we give an overview over the different approaches of digital image compositing, which are categorized as matting-based image compositing, gradient-based image compositing, and multi-resolution-based image compositing. The representative methods in each category are stated briefly, and then we compare and analyze their performance according to quality of compositing, robustness and computation time. Next, several state-of-art applications of image compositing are introduced as extension of this survey. Finally, the limitations, the research challenges and the future directions of image compositing are discussed.