## 2 Related Works
Information about the relative displacement of the environment (i.e., reflection dynamics) captured in the image sequence and separated from the video frames is used to help image sequences separate reflection layers and reduce reflection artifacts using temporal coherence constraints. Some studies proposed a recursive method for reflection removal based on a prediction-correction model [25], which is able to effectively decompose the reflected image into different layers (reflection and background layers) according to the principle of a real centrifuge. Some studies separated reflections and dirt by averaging images through a priori algorithms [23], and others introduced optical flow into specular reflection removal methods with high-speed cameras and stroboscopes [24], using the optical flow method to compensate for the noise at the specified location of the specular reflection due to inter-frame differences when the high-speed camera moves. Unlike the real experimental video that contains both object and camera motion, our experiments only need to bring two frames of the image sequence in the video frame into the model and use the feature extraction method to extract the same region in both frames, as the reflection removal action range for reflection removal and the video reflection can be removed by the inter-frame difference method for changing reflection images. Light field camera reflection removal. Although layer separation is irrelevant to conventional camera imaging, studies [26, 27, 28] demonstrate that light-field imaging can be beneficial to handle reflection removal. One study proposed a cue-no-reflection flash cue [27] to make distinguishing reflections easier for reflection removal and to avoid introducing artifacts in the presence of flash-only images when utilizing reflection-free cues by creating a specialized framework. Some of the studies have used reflection removal methods based on light field LF imaging [26, 28], using epipolar plane images, retrieving scene depth maps from low frequency images, dividing the gradient points of the images into background and reflection layers based on the depth values, and reconstructing background images using sparse optimization methods. The drawback of the method proposed in the study above is that a special light field camera is required for reflection removal. By contrast, our proposed method uses a camera for environmental information acquisition without the flash processing of the acquired image, which can simplify the image data acquisition process. Special function camera/sensor reflection removal. Special format images acquired by special cameras will have better results for the reflection removal of images. A study [30] proposed a learning framework-based reflection removal method for RGB images by directing the reflection suppression of active NIR images through a near-infrared (NIR) camera. There is also a study of reflection removal using data available on DP sensors [29]. This study simplifies the delineation of image gradients belonging to the background layer region by using out-of-focus-parallax cues that appear in the two sub-aperture views. However, most camera APIs currently do not provide access to this useful data, and the cost of this camera is high.