## 2 Related Works  
Single Image Reflection Removal (SIRR). Earlier researchers usually used a priori methods for image reflection removal, such as layer sparsity [2] and smoothing [3]. These methods mainly rely on the a priori asymmetry of the layers (reflection and background layers), and their applicability is limited by special cases. In recent years, machine learning-based reflection removal methods have prevailed. This approach learns the properties  
Figure 1: Physical (**left**) and mathematical (**middle**) image formation models of the image reflection removal method and the actual experimental presentation scenario (**right**). Types I and II ignore the refraction effect of thicker glass, while type III exists with thicker glass, which reflects and refracts light from the glass front object to different locations and exhibits different intensities, respectively, leading to the superimposed image phenomenon of the mirror front object in the reflective layer. of specific reflection removal methods from a large training set of data by well-designed machine learning networks [4]. To facilitate the learning of the learning network, some auxiliary cues are generally used to help train the learning network. For example, using image features to extract the reflection dynamics core module [5], the module is typically extracted from a VGG network to better predict the motion of the background layer and the reflection layer and propose the image reflection part. Some methods use hybrid networks [15] for image deblurring or image enhancement, after which gradient information [1, 4, 6, 7] and edge information [10] are explored; their information can be fused with reflection formation models [8] and Alpha hybrid masks [14], among others, to achieve the linear expression prediction of image reflections, generally choosing to use two stages [7, 9, 11] for prediction. Although machine learning-based methods achieve good results for radiation removal, they all try to solve the problem from the perspective of a single image. Multi-image reflection removal. Multi-image reflection removal generally captures multiple images of a scene in a predefined manner, an operation that makes the reflection removal problem easier to handle. Most of the methods studied for multi-image reflection removal are based on motion cues [17, 18, 19], which exploit the difference in motion between two layers of images of the same target scene captured from different viewpoints. Some of the multi-image reflection removal methods adhered to the sparse non-negative matrix decomposition (NMF) optimization by combining a color illumination reflection model with NMF optimization in a multi-image framework [20]. According to the synthetic data of this method, the real data images of non-dielectric materials that are obtained conform to the proposed reflection model. Some of them also combine image information and multiscale gradient information with human perception-inspired loss functions [21] to accomplish reflection removal from images with machine learning. There are also some studies that use multiple polarized images taken at different exposure times in order to achieve high accuracy reflection removal in high dynamic range scenes [16] and minimize the under/over exposure pixel problem of the captured images by introducing a weighted non-negative matrix decomposition (WNMF) with full variation regularization. These multi-image methods lead to better reflection removal results due to the availability of image information from different viewpoints. However, the acquisition of images takes a long time and is professional, which limits the applicability of non-professional personnel. Video reflection removal. Motion cues are also often used to handle video reflection removal [15, 16, 17, 18, 19, 20, 21, 22].