This paper addresses reflection removal, which is the task of separating
reflection components from a captured image and deriving the image with only
transmission components. Considering that the existence of the reflection
changes the polarization state of a scene, some existing methods have exploited
polarized images for reflection removal. While these methods apply polarized
images as the inputs, they predict the reflection and the transmission directly
as non-polarized intensity images. In contrast, we propose a
polarization-to-polarization approach that applies polarized images as the
inputs and predicts "polarized" reflection and transmission images using two
sequential networks to facilitate the separation task by utilizing the
interrelated polarization information between the reflection and the
transmission. We further adopt a recurrent framework, where the predicted
reflection and transmission images are used to iteratively refine each other.
Experimental results on a public dataset demonstrate that our method
outperforms other state-of-the-art methods.

这篇论文研究了反射去除的问题，提出了使用极化图像进行反射分离的极化 - 极化方法，并采用循环框架来逐步优化反射和传输分离的结果，实验证明该方法优于其他现有方法。

反射去除基于极化到极化网络的方法

Reflection Removal Using Recurrent Polarization-to-Polarization Network

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。