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Removing undesired reflection from an image captured through a glass surfaceis a very challenging problem with many practical application scenarios. Forimproving reflection removal, cascaded deep models have been usually adopted toestimate the transmission in a progressive manner. However, most existingmethods are still limited in exploiting the result in prior stage for guidingtransmission estimation. In this paper, we present a novel two-stage networkwith reflection-aware guidance (RAGNet) for single image reflection removal(SIRR). To be specific, the reflection layer is firstly estimated due to thatit generally is much simpler and is relatively easier to estimate.Reflectionaware guidance (RAG) module is then elaborated for better exploitingthe estimated reflection in predicting transmission layer. By incorporatingfeature maps from the estimated reflection and observation, RAG can be used (i)to mitigate the effect of reflection from the observation, and (ii) to generatemask in partial convolution for mitigating the effect of deviating from linearcombination hypothesis. A dedicated mask loss is further presented forreconciling the contributions of encoder and decoder features. Experiments onfive commonly used datasets demonstrate the quantitative and qualitativesuperiority of our RAGNet in comparison to the state-of-the-art SIRR methods.The source code and pre-trained model are available athttps://github.com/liyucs/RAGNet.

Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance