Extending Compressive Bilateral Filtering for Arbitrary Range Kernel

Yuto Sumiya, Norishige Fukushima, Kenjiro Sugimoto, Sei Ichiro Kamata

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Smoothing filters have been used for pre/post-processing in various fields, such as computer vision and computer graphics. Bilateral filtering (BF) has a typical edge-preserving filter for such applications. The main issue of BF is its computational cost. Constant-time BF (O(1) BF) is one of the solutions to this problem, and compressive BF is a kind of O(1) BF. Compressive BF has, however, a restriction that we can only use Gaussian kernel as a range kernel until now. In this paper, we propose the method to extend compressive BF to handle arbitrary range kernels. Experimental results show that our extension handles arbitrary range kernels, and becomes the number of convolutions into half.

本文言語English
ホスト出版物のタイトル2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
出版社IEEE Computer Society
ページ1018-1022
ページ数5
ISBN(電子版)9781728163956
DOI
出版ステータスPublished - 2020 10
イベント2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
継続期間: 2020 9 252020 9 28

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2020-October
ISSN(印刷版)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
国/地域United Arab Emirates
CityVirtual, Abu Dhabi
Period20/9/2520/9/28

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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