Image compositing is a task of combining regions from different images to
compose a new image. A common use case is background replacement of portrait
images. To obtain high quality composites, professionals typically manually
perform multiple editing steps such as segmentation, matting and foreground
color decontamination, which is very time consuming even with sophisticated
photo editing tools. In this paper, we propose a new method which can
automatically generate high-quality image compositing without any user input.
Our method can be trained end-to-end to optimize exploitation of contextual and
color information of both foreground and background images, where the
compositing quality is considered in the optimization. Specifically, inspired
by Laplacian pyramid blending, a dense-connected multi-stream fusion network is
proposed to effectively fuse the information from the foreground and background
images at different scales. In addition, we introduce a self-taught strategy to
progressively train from easy to complex cases to mitigate the lack of training
data. Experiments show that the proposed method can automatically generate
high-quality composites and outperforms existing methods both qualitatively and
quantitatively.

该论文介绍了一种利用前景和背景图片的上下文和颜色信息进行端到端优化并采用自学习策略来训练的，能够自动生成高质量图像合成的新方法，该方法基于拉普拉斯金字塔融合，提出了一种密集的多流融合网络，并在易于复杂的情况下进行渐进式训练，其在定性和定量上均优于现有方法。