Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal

Abstract

Reflections often obstruct the desired scene when taking photos through glass panels. Removing unwanted reflection automatically from the photos is highly desirable. Traditional methods often impose certain priors or assumptions to target particular type(s) of reflection such as shifted double reflection, thus have difficulty to generalize to other types. Very recently a deep learning approach has been proposed. It learns a deep neural network that directly maps a reflection contaminated image to a background (target) image (i.e.reflection free image) in an end to end fashion, and outperforms the previous methods. We argue that, to remove reflection truly well, we should estimate the reflection and utilize it to estimate the background image. We propose a cascade deep neural network, which estimates both the background image and the reflection. This significantly improves reflection removal. In the cascade deep network, we use the estimated background image to estimate the reflection, and then use the estimated reflection to estimate the background image, facilitating our idea of seeing deeply and bidirectionally.