Single Image Reflection Removal Based on GAN with Gradient Constraint

Ryo Abiko, Masaaki Ikehara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


When we take a picture through glass windows, the photographs are often degraded by undesired reflections. To separate reflection layer and background layer is an important problem for enhancing image quality. However, single-image reflection removal is a challenging process because of the ill-posed nature of the problem. In this paper, we propose a single-image reflection removal method based on generative adversarial network. Our network is an end-to-end trained network with four types of losses. It includes pixel loss, feature loss, adversarial loss and gradient constraint loss. We propose a novel gradient constraint loss in order to separate the background layer and the reflection layer clearly. Gradient constraint loss is applied in a gradient domain and it minimize the correlation between the background and reflection layer. Owing to the novel loss and our new synthetic dataset, our reflection removal method outperforms state-of-the-art methods in PSNR and SSIM, especially in real world images.

Original languageEnglish
Title of host publicationPattern Recognition - 5th Asian Conference, ACPR 2019, Revised Selected Papers
EditorsShivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
Number of pages16
ISBN (Print)9783030414030
Publication statusPublished - 2020
Event5th Asian Conference on Pattern Recognition, ACPR 2019 - Auckland, New Zealand
Duration: 2019 Nov 262019 Nov 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12046 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th Asian Conference on Pattern Recognition, ACPR 2019
Country/TerritoryNew Zealand


  • Deep learning
  • Generative adversarial network
  • Image separation
  • Reflection removal

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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