Extending Compressive Bilateral Filtering for Arbitrary Range Kernel

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

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

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE Computer Society
Pages1018-1022
Number of pages5
ISBN (Electronic)9781728163956
DOIs
Publication statusPublished - 2020 Oct
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: 2020 Sep 252020 Sep 28

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

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

Keywords

  • Constant-time bilateral filter
  • Fourier series expansion
  • O(1) bilateral filter
  • arbitrary range kernel
  • compressive bilateral filter

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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