Extending Compressive Bilateral Filtering for Arbitrary Range Kernel

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

doi:10.1109/ICIP40778.2020.9191123

Extending Compressive Bilateral Filtering for Arbitrary Range Kernel

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

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 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 language	English
Title of host publication	2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
Publisher	IEEE Computer Society
Pages	1018-1022
Number of pages	5
ISBN (Electronic)	9781728163956
DOIs	https://doi.org/10.1109/ICIP40778.2020.9191123
Publication status	Published - 2020 Oct
Event	2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates Duration: 2020 Sept 25 → 2020 Sept 28

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2020-October
ISSN (Print)	1522-4880

Conference

Conference	2020 IEEE International Conference on Image Processing, ICIP 2020
Country/Territory	United Arab Emirates
City	Virtual, Abu Dhabi
Period	20/9/25 → 20/9/28

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

Access to Document

10.1109/ICIP40778.2020.9191123

Cite this

Sumiya, Y., Fukushima, N., Sugimoto, K., & Kamata, S. I. (2020). Extending Compressive Bilateral Filtering for Arbitrary Range Kernel. In 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings (pp. 1018-1022). Article 9191123 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2020-October). IEEE Computer Society. https://doi.org/10.1109/ICIP40778.2020.9191123

Extending Compressive Bilateral Filtering for Arbitrary Range Kernel. / Sumiya, Yuto; Fukushima, Norishige; Sugimoto, Kenjiro et al.
2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings. IEEE Computer Society, 2020. p. 1018-1022 9191123 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sumiya, Y, Fukushima, N, Sugimoto, K & Kamata, SI 2020, Extending Compressive Bilateral Filtering for Arbitrary Range Kernel. in 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings., 9191123, Proceedings - International Conference on Image Processing, ICIP, vol. 2020-October, IEEE Computer Society, pp. 1018-1022, 2020 IEEE International Conference on Image Processing, ICIP 2020, Virtual, Abu Dhabi, United Arab Emirates, 20/9/25. https://doi.org/10.1109/ICIP40778.2020.9191123

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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.",

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N2 - 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.

AB - 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.

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Extending Compressive Bilateral Filtering for Arbitrary Range Kernel

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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