Stereoscopic image reflection removal based on Wasserstein Generative Adversarial Network

Xiuyuan Wang, Yikun Pan, Daniel P.K. Lun

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages148-151
Number of pages4
ISBN (Electronic)9781728180670
DOIs
Publication statusPublished - 1 Dec 2020
Event2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020 - Virtual, Macau, China
Duration: 1 Dec 20204 Dec 2020

Publication series

Name2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020

Conference

Conference2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020
Country/TerritoryChina
CityVirtual, Macau
Period1/12/204/12/20

Keywords

  • GAN
  • Reflection removal
  • stereoscopic images

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Media Technology

Cite this