# Transmission Removal from a Single OSN-Shared Glass Mixture Image
## 1 Introduction
In contrast, before and after the OSN transfer, the red box does not have a significant impact on the low-frequency information content of the image; however, during the acquisition of the high-frequency information in the green box, a part of the visibility will be lost, which means that when further analyzing the non-dominant reflection layer information, it is difficult to use the relevant information as a feature to output the reflection layer directly. Therefore, the manipulation of the image by the social media platform has caused significant changes to the original image after sharing. Figure 2: Visual comparison of glass mixture images shared over OSNs. (**a**) original image; (**b**) OSN shared image; (**c**) spectrogram of (**a**) after discrete cosine transform (DCT), and (**d**) spectrogram of (**b**) after DCT. To further verify the effect on the glass mixture image, we compared the results of the two images directly using an existing reflection layer extraction experiment, and the comparison results are shown in Figure 3. In general, the information of the OSN-shared image was incomplete compared to the results of the original input. For the original mixture image, the non-dominant reflection layer image becomes sparse as the information in each part of the image becomes tighter, due to the overlaying between the different image layers after the OSN platform propagation process, and we hope to solve the layer separation problem adaptively for an OSN-shared glass mixture image by building a network model methodology. Incorporating the motivations mentioned above, in this paper, we intend to improve the understanding of the original image after the OSN sharing using a network with an attention mechanism, and meanwhile, to regenerate the reflection layer image using an adversarial generation network architecture with enhanced feature correlation. The contributions of our work can be summarized as follows:  
* We consider the impact of social networks on the current transmission layer removal problem in glass mixture images, since the transmission removal problem is essential as an aid to shooting scene and photographer recognition;
* We propose a method for achieving image feature enhancement based on a self-attention mechanism, to ensure that the enhanced glass mixture image further improves the correlation between the reflection layer information;
* We design a two-stage generative adversarial network to remove the transmission layer. To fully use the valuable information in OSN-shared images, we also add attention to the transmission layer information in the mixture image and apply the estimated gradient information and the color patch information to the later stages of the transmission layer removal process;
* The method yielded a gain of 0.46 dB in PSNR, 0.016 in SSIM, and 0.057 in LPIPS, effectively improving the visual quality of the final extracted reflection layer images. The remainder of this paper is organized as follows: Section 2 introduces the related works about transmission removal and image processing shared over OSN. Sections 3 and 4 describe the proposed method and analyze the experimental results, respectively, and Section 5 concludes this paper. Figure 3: An example of reflection layer extraction. (**a**) ground-truth reflection layer image; (**b**,**c**) predicted reflection layer from the original and the OSN-shared glass mixture images, respectively.