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

TL;DR

This paper introduces RAGNet, a two-stage deep learning model that effectively removes reflections from single images by estimating reflection layers first and then guiding transmission estimation with reflection-aware guidance, outperforming existing methods.

Contribution

The paper proposes a novel two-stage network with reflection-aware guidance for improved single image reflection removal, incorporating a new guidance module and mask loss for better reflection suppression.

Findings

RAGNet outperforms state-of-the-art methods on five datasets.

The reflection-aware guidance effectively exploits estimated reflection information.

The method achieves superior qualitative and quantitative results.

Abstract

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.