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