High Reflection Removal Using CNN with Detection and Estimation

Isana Funahashi, Naoki Yamashita, Taichi Yoshida, Masaaki Ikehara

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

Abstract

In this paper, we propose a method of reflection removal that reduces high intensity reflection for single image. Various methods of reflection removal have been proposed, but they fail to reduce the high reflections due to their assumption. To tackle this issue, the proposed method detects the target areas with high reflections by the proposed convolutional neural network (CNN) model and estimates their background information by inpainting. It is observed that the reflection is strongly blurred because of its physical property, and hence the proposed CNN model utilizes edge features of pixels for the detection. In simulation, we compare state-of-the-art methods of reflection removal with and without the proposed method for natural images, and the proposed method improves peak signal-to-noise ratio and perceptual quality.

Original languageEnglish
Title of host publication2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1381-1385
Number of pages5
ISBN (Electronic)9789881476890
Publication statusPublished - 2021
Event2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
Duration: 2021 Dec 142021 Dec 17

Publication series

Name2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings

Conference

Conference2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Country/TerritoryJapan
CityTokyo
Period21/12/1421/12/17

Keywords

  • convolutional neural network
  • image inpainting
  • Single image reflection removal

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Instrumentation

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