High Reflection Removal Using CNN with Detection and Estimation

Isana Funahashi, Naoki Yamashita, Taichi Yoshida, Masaaki Ikehara

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1381-1385
ページ数5
ISBN(電子版)9789881476890
出版ステータスPublished - 2021
イベント2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
継続期間: 2021 12月 142021 12月 17

出版物シリーズ

名前2021 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
国/地域Japan
CityTokyo
Period21/12/1421/12/17

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理
  • 器械工学

フィンガープリント

「High Reflection Removal Using CNN with Detection and Estimation」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル