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.

翻译：从玻璃表面摄取的图像中去除不理想的反射是一个非常困难的问题,许多实际应用情景都存在一个非常棘手的问题。为了改进反射去除,通常采用一系列深层模型来逐步估计传输情况,然而,大多数现有方法仍然有限,无法在指导传输估计的先前阶段利用结果;在本文件中,我们提出了一个具有反射觉导的新颖的两阶段网络,为单一图像反射去除提供反射指导(RAGNet),具体地说,对反射层进行初步估计,因为其一般比较简单,比较容易估计。然后,为更好地利用预测传输层的估计反射指导(RAG)模块,对五种常用数据集进行实验,展示了预测传输层的估计反射层的估计反射情况。通过纳入估计的反射和观察的地貌图图图,可以使用RAGAG(RA)用于减轻观察的反射效果,以及(二)产生部分反射面遮罩,以缓解从线性组合假设中脱影的影响。还提出专门的遮罩损失,以调解码和解码特性。然后,对五种常用的数据模型进行实验,以显示SARGNet/RAGRAG)在RAG/RAG/RU的定量和定性中的现有源源/RARM/RARM/RU/RU的校前的比较。