  Reflection is common in images capturing scenes behind a glass window, which
is not only a disturbance visually but also influence the performance of other
computer vision algorithms. Single image reflection removal is an ill-posed
problem because the color at each pixel needs to be separated into two values,
i.e., the desired clear background and the reflection. To solve it, existing
methods propose priors such as smoothness, color consistency. However, the
low-level priors are not reliable in complex scenes, for instance, when
capturing a real outdoor scene through a window, both the foreground and
background contain both smooth and sharp area and a variety of color. In this
paper, inspired by the fact that human can separate the two layers easily by
recognizing the objects, we use the object semantic as guidance to force the
same semantic object belong to the same layer. Extensive experiments on
different datasets show that adding the semantic information offers a
significant improvement to reflection separation. We also demonstrate the
applications of the proposed method to other computer vision tasks.
