Abstract
this paper presents an efficient constant-time bilateral filter that produces a near-optimal performance tradeoff between approximate accuracy and computational complexity without any complicated parameter adjustment, called a compressive bilateral filter (CBLF). The constant-time means that the computational complexity is independent of its filter window size. Although many existing constant-time bilateral filters have been proposed step-by-step to pursue a more efficient performance tradeoff, they have less focused on the optimal tradeoff for their own frameworks. It is important to discuss this question, because it can reveal whether or not a constant-time algorithm still has plenty room for improvements of performance tradeoff. This paper tackles the question from a viewpoint of compressibility and highlights the fact that state-of-the-art algorithms have not yet touched the optimal tradeoff. The CBLF achieves a near-optimal performance tradeoff by two key ideas: 1) an approximate Gaussian range kernel through Fourier analysis and 2) a period length optimization. Experiments demonstrate that the CBLF significantly outperforms state-of-the-art algorithms in terms of approximate accuracy, computational complexity, and usability.
| Original language | English |
|---|---|
| Article number | 7120121 |
| Pages (from-to) | 3357-3369 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 24 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 2015 Nov 1 |
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Keywords
- Compressibility
- Constant-time bilateral filtering
- Edge-preserving smoothing
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Software
Cite this
Compressive bilateral filtering. / Sugimoto, Kenjiro; Kamata, Seiichiro.
In: IEEE Transactions on Image Processing, Vol. 24, No. 11, 7120121, 01.11.2015, p. 3357-3369.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Compressive bilateral filtering
AU - Sugimoto, Kenjiro
AU - Kamata, Seiichiro
PY - 2015/11/1
Y1 - 2015/11/1
N2 - this paper presents an efficient constant-time bilateral filter that produces a near-optimal performance tradeoff between approximate accuracy and computational complexity without any complicated parameter adjustment, called a compressive bilateral filter (CBLF). The constant-time means that the computational complexity is independent of its filter window size. Although many existing constant-time bilateral filters have been proposed step-by-step to pursue a more efficient performance tradeoff, they have less focused on the optimal tradeoff for their own frameworks. It is important to discuss this question, because it can reveal whether or not a constant-time algorithm still has plenty room for improvements of performance tradeoff. This paper tackles the question from a viewpoint of compressibility and highlights the fact that state-of-the-art algorithms have not yet touched the optimal tradeoff. The CBLF achieves a near-optimal performance tradeoff by two key ideas: 1) an approximate Gaussian range kernel through Fourier analysis and 2) a period length optimization. Experiments demonstrate that the CBLF significantly outperforms state-of-the-art algorithms in terms of approximate accuracy, computational complexity, and usability.
AB - this paper presents an efficient constant-time bilateral filter that produces a near-optimal performance tradeoff between approximate accuracy and computational complexity without any complicated parameter adjustment, called a compressive bilateral filter (CBLF). The constant-time means that the computational complexity is independent of its filter window size. Although many existing constant-time bilateral filters have been proposed step-by-step to pursue a more efficient performance tradeoff, they have less focused on the optimal tradeoff for their own frameworks. It is important to discuss this question, because it can reveal whether or not a constant-time algorithm still has plenty room for improvements of performance tradeoff. This paper tackles the question from a viewpoint of compressibility and highlights the fact that state-of-the-art algorithms have not yet touched the optimal tradeoff. The CBLF achieves a near-optimal performance tradeoff by two key ideas: 1) an approximate Gaussian range kernel through Fourier analysis and 2) a period length optimization. Experiments demonstrate that the CBLF significantly outperforms state-of-the-art algorithms in terms of approximate accuracy, computational complexity, and usability.
KW - Compressibility
KW - Constant-time bilateral filtering
KW - Edge-preserving smoothing
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U2 - 10.1109/TIP.2015.2442916
DO - 10.1109/TIP.2015.2442916
M3 - Article
VL - 24
SP - 3357
EP - 3369
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 11
M1 - 7120121
ER -