Reflection Removal Using Recurrent Polarization-to-Polarization Network
Wenjiao Bian Yusuke Monno Masatoshi Okutomi
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.