## 1 Introduction  
Reflection removal involves separating the layers of an image into the background and reflection layers. There are various methods for removing reflections, including single image reflection removal (SIRR) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], multi-image reflection removal [16, 17, 18, 19, 20, 21], video reflection removal [22, 23, 24, 25], light field camera reflection removal [26, 27, 28, 29], and special function camera (sensor) reflection removal [30, 31]. The image removal model is shown in Figure 1.  
Recent research has revealed that machine learning networks [9, 10] that are designed well can learn the distinguishing features of specific reflection removal methods from a vast amount of training set data. For instance, by using image features for the semantic segmentation of images [6] or extracting core modules for reflection dynamics [5], usually from VGG networks, these networks can better predict the movement of the background and reflection layers and suggest image reflection parts. Typically, prediction is carried out in two stages [7, 9, 11]. In another study, a solution was proposed to minimize the issue of under/over exposed pixels in captured images. This was achieved by using multiple polarized images taken at different exposure times [16] and introducing the weighted non-negative matrix decomposition of full change regularization. It should be noted, however, that these methods often require a longer sample training time.  
In this paper, we provide a method for removing reflection layer areas from videos that does not require training. It uses an image completion strategy and a lighting correction mechanism. This process has two essential components: (1) The recognition of reflection regions, which compares the two video frames before and after the region and lowers the error rate of recognition by using a lighting correction technique; (2) Pixel substitution methods in the reflection removal process in order to cover the intended reflection area and make the reflection removal procedure simpler. The target area recognition step of this method's detection speed and reflection removal integrity may be guaranteed by coupling these two activities. In contrast to [4; 16; 26; 28], our technique does not need the building of a complicated machine learning network or a substantial amount of sample training, and the process for acquiring pictures is also rather straightforward. Our contributions are summarized as follows:  
1. We propose a video reflection removal method based on the combination of image complementation and illumination compensation, which has a 5.2% higher reflection region removal accuracy and operation speed than without adding illumination compensation.
2. We propose an illumination compensation mechanism based on a combination of luminance adjustment and gamma correction for reflective region extraction by joint MSER region matching.  
The rest of this paper is organized as follows: In Section 2, we introduce an illumination compensation mechanism based on a combination of brightness adjustment and gamma correction, as well as a reflection region extraction method based on MSER region matching, with a focus on the reflection region removal method based on image completion and illumination compensation fusion. This paper analyzes the effectiveness of a reflection region removal method based on image completion and lighting compensation fusion. By comparing the image completion-based reflection region removal method with the relative motion-based reflection region removal method, the superiority of our method was found. In Section 3, we obtain the superiority of our method through comparative experiments.