# Single Image Reflection Removal Based on Residual Attention Mechanism
## 1 Introduction  
Images captured through glass have a noticeable layer of reflection due to the shooting angle and light intensity. Image reflection can reduce the image quality and adversely affect the results of computer vision tasks, such as image classification and object detection. Accordingly, reflection layers are expected to be removed to obtain clear images. In this study, the research objective of image reflection removal is to predict the transmission layer T from a given reflection image I. According to [1], let I be the reflection image, T the transmission layer and R the reflection layer, then the reflection image can be approximately modeled as a combination of T and R. It can be seen that to any I, both the transmission layer T and the reflection layer R are unknown. As there are no additional constraints or priors, the equation has infinite feasible solutions. To solve this problem, it is imperative for researchers to impose constraints and artificial priors on the solution space, thus a separation of the reflection image closer to an ideal target solution can be obtained. As for the ill-posed problem, [2] proposed the concept of relative smoothness for reflection image separation. That is, the reflection layer is considered to be smoother relative to the transmission layer, so a smooth gradient is applied to the objective function of the reflection layer, and a sparse gradient is applied to the objective function of the transmission layer. There are other solutions proposed. For example, [3] introduced the use of ghosting cues that exploit the asymmetry between layers, thus helping to reduce the discomfort of eliminating reflections in images taken through thick glass. The authors of [4] proposed a simple yet effective reflection-free cueto remove reflection with robustness from a pair of flash and ambient (no-flash) images. However, if the camera is placed far from the subject which cannot be reached by the flashlight, the flash-only image obtained at this time may be a black image. As can be seen above, the physical model-based approach consists of developing a mathematical model for reflection removal and using the model to estimate the reflection parameters. These methods usually require a lot of manual intervention to tune the model parameters and may not be able to handle complex reflection scenarios. In addition, these a priori designs have high requirements and limited application in reflection removal. As machine learning research develops and computer hardware devices improve in performance, more and more methods use machine learning techniques (e.g., neural networks) to solve reflection removal problems by applying the learned models to new images. These methods are often more flexible than physical model-based methods in handling more complex reflection scenarios. However, most of the existing methods for deep learning methods have more complex network structures with a large number of parameters, thus requiring a long training time. Furthermore, these methods fail to make good use of the spatial intensity inhomogeneity of the reflection and transmission layers. Driven by the above reasons, we would like to develop a new image reflection removal method. We consider reflection removal as a typical image separation problem. Therefore, it can be assumed that the transmission and reflection layers correspond to specific channels, which implies the need to enhance the model for channel feature representation. However, as for the reflection removal task, it is not sufficient to rely only on channel concerns. Moreover, the reflection layer can be viewed as a translucent soft mask over the transmission layer, and the spatial intensity of both layers is inhomogeneous, depending on the camera angle and light intensity. To better focus on the inhomogeneity of the spatial intensity distribution, we integrated a residual attention module into the encoder used in our method to combine channel attention with spatial attention.