# Single Image Reflection Removal Based on Residual Attention Mechanism
## 2 Related Work
Limited by the linear image synthesis paradigm, the method cannot be extended to other real scenes. In 2018, [PERSON] [22] proposed a new deep convolutional encoder-decoder method to remove reflections from images by learning mappings between reflective and non-reflectiveimage pairs. The neural network was trained to model the physical formation of reflections in images. In addition, a large number of photo-realistic reflection-contaminated images were synthesized from reflection-free images collected on the network. In the same year, [PERSON] et al. (2019) argued that the reflective layer information existed in the surface layer and the transmission layer information in deeper layers, so they designed a two-stage network CRRN, where the first stage infers the gradient of the transmission layer and the second stage uses the output of the first stage to predict the final transmission layer. In this way, the CRRN integrates the surface layer information and the gradient information of multiple degrees to guide the reflection separation. The following year, [PERSON] (2019) proposed a network, CoRRN, with features sharing the strategy based on CRRN to tackle this problem, combining image contextual information with multi-scale gradient information. [PERSON] (2019) proposed a synthetic network, in which they used one decoder and three encoders as a three-streamline reflection removal network to predict the transmission layers, generating reflection images from them with the predicted alpha mixing mask in a beyond-linear manner. [PERSON] et al. proposed the IBLCN (2019), a recursive LSTM-based network for the successive refinement of the reflection and transmission layers. In IBLCN, two pairs of decoder-encoder structures are used to predict the transmission and reflection layers, respectively. [PERSON] (2019) proposed a position-aware reflection removal network, which has a reflection detection module that accepts multi-scale Laplace features as input, then outputs a reflection confidence map. The confidence map indicates the region dominated by the reflection layer, and the input image is reorganized to input into the separation network. The ERRNet proposed by [PERSON] (2019) introduces a multi-scale channel attention mechanism in the decoder of the network so that it can reorganize and enhance the different channel features. The above learning-based approaches have achieved significant improvements in individual image reflection removal compared to the traditional optimization-based approaches. To sum up, although methods based on convolutional neural networks have outperformed traditional polarization-based methods, only ERRNet (2019) exploits the channel attention mechanism, which values the importance of different features. However, ERRNet does not harness spatial attention, nor does it pay attention to which spatial regions of the feature map are more important. In this paper, we try to construct a residual attention module using a Convolutional Block Attention Module (CBAM) (2019) and residual networks that combine spatial attention and channel attention to design an iterative dual streamline network. The reflection removal problem is solved by continuously refining the reflection-related information in the generative and discriminative networks in each iteration.