Reflection Removal Using Recurrent Polarization-to-Polarization Network
This addresses the problem of image quality degradation due to reflections for computer vision applications, but it is incremental as it builds on existing polarization-based methods.
The paper tackles reflection removal by separating reflection and transmission components from images using a novel polarization-to-polarization approach with recurrent networks, achieving state-of-the-art performance on a public dataset.
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.