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.

翻译：本文针对反射去除问题展开研究，该任务旨在从拍摄图像中分离出反射分量，仅保留透射分量图像。考虑到反射的存在会改变场景的偏振状态，现有方法已利用偏振图像进行反射去除。尽管这些方法采用偏振图像作为输入，但它们直接预测非偏振的反射和透射强度图像。与之不同，我们提出一种偏振到偏振的处理方法，该方法以偏振图像为输入，通过两个级联网络分别预测“偏振化”的反射和透射图像，从而利用反射与透射之间相互关联的偏振信息来促进分离任务。我们进一步采用循环框架，将预测得到的反射和透射图像用于迭代优化彼此性能。在公开数据集上的实验结果表明，我们的方法优于其他最新方法。