Constant-time Bilateral Filter using Spectral Decomposition

Sugimoto, K.; Breckon, T.P.; Kamata, S.

Abstract

This paper presents an efficient constant-time bilateral filter where constant-time means that computational complexity is independent of filter window size. Many state-of-the-art constant-time methods approximate the original bilateral filter by an appropriate combination of a series of convolutions. It is important for this framework to optimize the performance tradeoff between approximate accuracy and the number of convolutions. The proposed method achieves the optimal performance tradeoff in a least-squares manner by using spectral decomposition under the assumption that images consist of discrete intensities such as 8-bit images. This approach is essentially applicable to arbitrary range kernel. Experiments show that the proposed method outperforms state-of-the-art methods in terms of both computational complexity and approximate accuracy.

Citation

Sugimoto, K., Breckon, T., & Kamata, S. (2016). Constant-time Bilateral Filter using Spectral Decomposition. In 2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, Arizona, USA ; proceedings (3319-3323). https://doi.org/10.1109/icip.2016.7532974

• Conference Name: 2016 IEEE International Conference on Image Processing (ICIP).
• Conference Location: Phoenix, AZ, USA
• Start Date: Sep 25, 2016
• End Date: Sep 28, 2016
• Acceptance Date: Jul 12, 2016
• Online Publication Date: Aug 19, 2016
• Publication Date: Aug 19, 2016
• Deposit Date: Oct 3, 2016
• Publicly Available Date: Oct 6, 2016
• Pages: 3319-3323
• Series ISSN: 2381-8549
• Book Title: 2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, Arizona, USA ; proceedings.
• DOI: https://doi.org/10.1109/icip.2016.7532974
• Related Public URLs: http://community.dur.ac.uk/toby.breckon/publications/papers/sugimoto16bilateral.pdf
• Additional Information: Date of Conference: 25-28 Sept. 2016