@inproceedings{17bc976b7be043508a8461bd4e6d9108,
title = "Stereoscopic image reflection removal based on Wasserstein Generative Adversarial Network",
abstract = "Reflection removal is a long-standing problem in computer vision. In this paper, we consider the reflection removal problem for stereoscopic images. By exploiting the depth information of stereoscopic images, a new background edge estimation algorithm based on the Wasserstein Generative Adversarial Network (WGAN) is proposed to distinguish the edges of the background image from the reflection. The background edges are then used to reconstruct the background image. We compare the proposed approach with the state-of-the- art reflection removal methods. Results show that the proposed approach can outperform the traditional single-image based methods and is comparable to the multiple-image based approach while having a much simpler imaging hardware requirement.",
keywords = "GAN, Reflection removal, stereoscopic images",
author = "Xiuyuan Wang and Yikun Pan and Lun, {Daniel P.K.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020 ; Conference date: 01-12-2020 Through 04-12-2020",
year = "2020",
month = dec,
day = "1",
doi = "10.1109/VCIP49819.2020.9301892",
language = "English",
series = "2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "148--151",
booktitle = "2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020",
}