Transmission Removal from a Single OSN-Shared Glass Mixture Image
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Published:2023-11-28
Issue:23
Volume:13
Page:12779
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Yao Heng1ORCID, Li Zhen1, Qin Chuan1
Affiliation:
1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Abstract
Photographs taken through glass often reflect the photographer or the surroundings, which is very helpful in uncovering information about the photograph. Various lossy operations performed on images over online social networks (OSNs), such as compression and resampling, pose a great challenge for transmission layer removal. This paper proposes a self-attention-based architecture for image enhancement over OSNs, to ensure that the downloaded glass mixture image can show more information about the reflection layer than the original image. Transmission layer removal is then achieved using a two-stage generative adversarial network. We also add attention to the transmission layer in the mixture image and use the gradient and color block information in the next stage to extract the reflection layer. This method yielded a gain of 0.46 dB in PSNR, 0.016 in SSIM, and 0.057 in LPIPS, resulting in an effective improvement in the visual quality of the final extracted reflection layer images.
Funder
National Natural Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference31 articles.
1. Ra, M.R., Govindan, R., and Ortega, A. (2013, January 2–5). P3: Toward {Privacy-Preserving} Photo Sharing. Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13), Lombard, IL, USA. 2. Ning, J., Singh, I., Madhyastha, H.V., Krishnamurthy, S.V., Cao, G., and Mohapatra, P. (2014, January 29–31). Secret message sharing using online social media. Proceedings of the IEEE Conference on Communications and Network Security, San Francisco, CA, USA. 3. Sun, W., Zhou, J., Lyu, R., and Zhu, S. (2016, January 15–19). Processing-aware privacy-preserving photo sharing over online social networks. Proceedings of the 24th ACM international conference on Multimedia, Amsterdam, The Netherlands. 4. Face image reflection removal;Wan;Int. J. Comput. Vis.,2021 5. Image reflection removal using end-to-end convolutional neural network;Li;IET Image Process.,2020
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