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 |
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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 |
Keywords
- Compressibility
- Constant-time bilateral filtering
- Edge-preserving smoothing
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design