We present a novel formulation to removing reflection from polarized images
in the wild. We first identify the misalignment issues of existing reflection
removal datasets where the collected reflection-free images are not perfectly
aligned with input mixed images due to glass refraction. Then we build a new
dataset with more than 100 types of glass in which obtained transmission images
are perfectly aligned with input mixed images. Second, capitalizing on the
special relationship between reflection and polarized light, we propose a
polarized reflection removal model with a two-stage architecture. In addition,
we design a novel perceptual NCC loss that can improve the performance of
reflection removal and general image decomposition tasks. We conduct extensive
experiments, and results suggest that our model outperforms state-of-the-art
methods on reflection removal.

提出一种新颖的方法，使用极化图像来消除反射，该方法解决了现有反射去除数据集中由于玻璃折射导致反射区域与无反射区域的不完全对齐问题。该方法还采用了双阶段架构的极化反射消除模型和一种新的感知 NCC 损失，可以在反射消除和图像分解任务方面提高性能，并在大量的实验中证明其胜过现有的反射消除方法。