  Removing undesired reflection from an image captured through a glass surface
is a very challenging problem with many practical application scenarios. For
improving reflection removal, cascaded deep models have been usually adopted to
estimate the transmission in a progressive manner. However, most existing
methods are still limited in exploiting the result in prior stage for guiding
transmission estimation. In this paper, we present a novel two-stage network
with reflection-aware guidance (RAGNet) for single image reflection removal
(SIRR). To be specific, the reflection layer is firstly estimated due to that
it generally is much simpler and is relatively easier to estimate.
Reflectionaware guidance (RAG) module is then elaborated for better exploiting
the estimated reflection in predicting transmission layer. By incorporating
feature maps from the estimated reflection and observation, RAG can be used (i)
to mitigate the effect of reflection from the observation, and (ii) to generate
mask in partial convolution for mitigating the effect of deviating from linear
combination hypothesis. A dedicated mask loss is further presented for
reconciling the contributions of encoder and decoder features. Experiments on
five commonly used datasets demonstrate the quantitative and qualitative
superiority of our RAGNet in comparison to the state-of-the-art SIRR methods.
The source code and pre-trained model are available at
https://github.com/liyucs/RAGNet.
