# Transmission Removal from a Single OSN-Shared Glass Mixture Image
## 1 Introduction  
When taking a photograph through glass, we can obtain a glass mixture image that contains a transmission layer and a reflection layer. The operation of recovering the reflection layer from the mixture image is defined as the transmission removal problem. Figure 1 illustrates a schematic diagram of the glass mixture image separation problem, where transmission removal (also referred to as reflection separation or reflection extraction) focuses more on exploring features that are not easily detectable in the blended image. Studies based on transmission layer removal can be effective in aiding the identification of photo capture scenarios (e.g., photographer and location information). In traditional methods based on statistical analysis, exploring the differences between the two layers is a prerequisite for achieving the removal of one of the layers. Today, everyone can share daily images over online social network (OSN) platforms, and according to statistics, the amount of image data shared o  
Figure 1: Schematic diagram of glass mixture image separation problem. 1.3 billion images per day (How many pictures are there (2023): Statistics, trends, and forecasts. Available: [[https://photutorial.com/photos-statistics/](https://photutorial.com/photos-statistics/)]([https://photutorial.com/photos-statistics/](https://photutorial.com/photos-statistics/)), accessed on 19 November 2023), in addition to some of the other OSNs, such as WhatsApp, Facebook, Snapchat, and WeChat. Therefore, studying social media shared images can help understand the practical significance of interpersonal activities. When an image is uploaded to a platform, the platform will accordingly perform a series of processing processes on the image [1; 2; 3]. These operations can be roughly summarized as follows: (1) resize the image: images uploaded by users may have various sizes, and one way to process an image is to resize it to make it more suitable for displaying on social media; (2) enhance image quality: uploaded images may appear blurry or unclear due to low light or other reasons, and OSNs can enhance the quality of images through manipulating them, such as increasing contrast, adjusting brightness and color saturation; (3) crop the image: uploaded images may contain unnecessary information, such as backgrounds or other clutter. By cropping an image, the subject can be highlighted and made more appealing; (4) add filters: filters are often used on OSNs to make images more attractive, and they can also change the color and contrast of images and add special effects, such as blurring or black and white effects; (5) recognize faces: social media can use face recognition techniques to identify faces in images and manipulate them; (6) protect user privacy: social media can protect user privacy by processing images. For example, social media can use blurring techniques to hide sensitive information in images, such as addresses, license plate numbers, or faces. This means that the re-downloaded image data are not exactly the same as the original image, in terms of pixels and structure, which greatly increases the difficulty of understanding the information contained in the image. Therefore, it is a challenge to effectively remove the transmission layer when a glass mixture image is uploaded to OSNs. This paper considers a scenario in which people share images containing glass reflections over OSNs. Figure 2 shows a visual comparison of OSN shared images, where from the point of view of visual quality, it is difficult to determine the difference between the two images (a) and (b), which indicates that the overall distribution does not change during the network transfer, so we analyze the differences in the frequency domain. We perform DCT on these two images separately, and according to (c) and (d), the red box indicates the low-frequency information of the image, while outside the red box mainly contains the high-frequency information of the image, which is selected and marked with a green box for the convenience of analysis.