Image Reflection Removal Using the Wasserstein Generative Adversarial Network

Tingtian Li, Daniel P.K. Lun

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

2 Citations (Scopus)

Abstract

Imaging through a semi-transparent material such as glass often suffers from the reflection problem, which degrades the image quality. Reflection removal is a challenging task since it is severely ill-posed. Traditional methods, while all require long computation time on minimizing different objective functions with huge matrices, do not necessarily give satisfactory performance. In this paper, we propose a novel deep-learning based method to allow fast removal of reflection. Similar to the traditional multiple-image approaches, the proposed algorithm first captures the multi-view images of a scene. Then the images are fed to a convolutional neural network to obtain the depth information along the edges of the image. It is sent to a Wasserstein generative adversarial networks (WGAN) for estimating the edges of the background. Finally, the background edges are used in another WGAN to reconstruct the background image. Experimental results show that the proposed method can achieve state-of-the-art performance, and is significantly faster than the traditional methods due to the use of the deep learning methods.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7695-7699
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • blind image separation
  • Reflection removal
  • Wasserstein generative adversarial network

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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