# Single Image Reflection Removal Based on Residual Attention Mechanism
## 2 Related Work  
Research on image reflection removal dates back decades. Previous studies in this research field can be classified into multi-view reflection removal and video-based reflection removal [6; 7; 8; 9; 10; 11], and single image reflection removal (SIRR) [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. Single image reflection removal is a professionally demanding problem with a single reflected image as the input. In 1990, [PERSON] [15] proposed a simple Fresnel reflection model where a polarizer is placed in front of the camera sensor and images are taken from different directions to determine the polarization state of the reflected light. The polarization state of the reflected light received at the pixel can be obtained by observing the polarization state of the reflected light transmitted through the polarizer which is a function of the polarizer direction. It enables the separation of the reflected light from the transmitted light via a polarizing material before image acquisition. In 2002, [PERSON] [16] proposed the solution of superimposing two natural images and then separating them, which was then applied to the image reflection removal task, but the reflection removal results achieved were not satisfactory. Then, in 2004, [PERSON] and [PERSON] [17] proposed an algorithm that performs the decomposition using an extremely simple form of prior knowledge. That is, when the input is a single image, the algorithm decomposes it into two images by minimizing the total number of edges and corners, which, however, did not separate the reflective layers well. Subsequently, in 2007, [PERSON] [18] further refined the algorithm using a gradient sparsity prior and a superconducting filter. He optimized this sparsity prior using an iterative reweighted least squares (IRLS) method, in addition to a manual labeling aid employed to mark which pixel domains belong to the transmission or reflection layers. Nevertheless, it increased the workload on labeling. In 2016, [PERSON] [19] proposed a visual depth-guided reflection removal method based on Kullback-Leibler scattering to compute multi-scale scenes to efficiently classify edge pixels. Additionally, based on the edge map results of edge classification, the reflection and transmission layers are then separated using [PERSON] and [PERSON]'s method. Then, in 2017, [PERSON] [20] proposed a new method for suppressing reflections based on the Laplace data fidelity term and the L\({}_{0}\) gradient sparsity term which are imposed on the output. This method does not try to separate the transmission and reflection layers as in previous work, but it suppresses the reflections from the input image as much as possible. However, as mass iteration training is required, efficiency needs to be improved. These traditional methods based on optimization have achieved some good results, but the image acquisition process is often affected by factors such as lighting, surrounding scenery, and framing angle, making real reflection scenes diverse and complex, which brings problems in using these methods. Meanwhile, relying on specific a priori methods has limitations. The rapid development of deep learning technology enables researchers to use deep neural networks to address SIRR-related problems. For example, in 2017, [PERSON] [21] was the first to explore the use of deep neural network models to solve the reflection removal problem. He used edge maps as auxiliary cues to guide the separation of the reflective and transmission layers, as well as a linear approach to synthesizing reflective images for the model training. However, the method is rather problematic because it ignores the high-dimensional semantic information of the image that helps to remove reflections. Meanwhile, low-dimensional semantic information cannot be used to guide the separation of reflection images with blurred colors. In 2018, [PERSON] [1] proposed a deep neural network with perceptual loss and exclusion loss to perform single image reflection separation.