A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences
- Author
- Bart Goossens (UGent) , Hiep Luong (UGent) , Jan Aelterman (UGent) , Aleksandra Pizurica (UGent) and Wilfried Philips (UGent)
- Organization
- Abstract
- Abstract. The NLMeans filter, originally proposed by Buades et al., is a very popular filter for the removal of white Gaussian noise, due to its simplicity and excellent performance. The strength of this filter lies in exploiting the repetitive character of structures in images. However, to fully take advantage of the repetitivity a computationally extensive search for similar candidate blocks is indispensable. In previous work, we presented a number of algorithmic acceleration techniques for the NLMeans filter for still grayscale images. In this paper, we go one step further and incorporate both temporal information and color information into the NLMeans algorithm, in order to restore video sequences. Starting from our algorithmic acceleration techniques, we investigate how the NLMeans algorithm can be easily mapped onto recent parallel computing architectures. In particular, we consider the graphical processing unit (GPU), which is available on most recent computers. Our developments lead to a high-quality denoising filter that can process DVD-resolution video sequences in real-time on a mid-range GPU.
- Keywords
- REMOVAL, NOISE, IMAGE, NONLOCAL MEANS, SCALE MIXTURES, WAVELET DOMAIN
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-1246898
- MLA
- Goossens, Bart, et al. “A GPU-Accelerated Real-Time NLMeans Algorithm for Denoising Color Video Sequences.” Lecture Notes in Computer Science, edited by Jacques Blanc Talon et al., vol. 6475, Springer, 2010, pp. 46–57, doi:10.1007/978-3-642-17691-3_5.
- APA
- Goossens, B., Luong, H., Aelterman, J., Pizurica, A., & Philips, W. (2010). A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences. In J. Blanc Talon, D. Bone, W. Philips, D. Popescu, & P. Scheunders (Eds.), Lecture Notes in Computer Science (Vol. 6475, pp. 46–57). https://doi.org/10.1007/978-3-642-17691-3_5
- Chicago author-date
- Goossens, Bart, Hiep Luong, Jan Aelterman, Aleksandra Pizurica, and Wilfried Philips. 2010. “A GPU-Accelerated Real-Time NLMeans Algorithm for Denoising Color Video Sequences.” In Lecture Notes in Computer Science, edited by Jacques Blanc Talon, Don Bone, Wilfried Philips, Dan Popescu, and Paul Scheunders, 6475:46–57. Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-17691-3_5.
- Chicago author-date (all authors)
- Goossens, Bart, Hiep Luong, Jan Aelterman, Aleksandra Pizurica, and Wilfried Philips. 2010. “A GPU-Accelerated Real-Time NLMeans Algorithm for Denoising Color Video Sequences.” In Lecture Notes in Computer Science, ed by. Jacques Blanc Talon, Don Bone, Wilfried Philips, Dan Popescu, and Paul Scheunders, 6475:46–57. Berlin, Germany: Springer. doi:10.1007/978-3-642-17691-3_5.
- Vancouver
- 1.Goossens B, Luong H, Aelterman J, Pizurica A, Philips W. A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences. In: Blanc Talon J, Bone D, Philips W, Popescu D, Scheunders P, editors. Lecture Notes in Computer Science. Berlin, Germany: Springer; 2010. p. 46–57.
- IEEE
- [1]B. Goossens, H. Luong, J. Aelterman, A. Pizurica, and W. Philips, “A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences,” in Lecture Notes in Computer Science, Sydney, Australia, 2010, vol. 6475, pp. 46–57.
@inproceedings{1246898,
abstract = {{Abstract. The NLMeans filter, originally proposed by Buades et al., is a very popular filter for the removal of white Gaussian noise, due to its simplicity and excellent performance. The strength of this filter lies in exploiting the repetitive character of structures in images. However, to fully take advantage of the repetitivity a computationally extensive search for similar candidate blocks is indispensable. In previous work, we presented a number of algorithmic acceleration techniques for the NLMeans filter for still grayscale images. In this paper, we go one step further and incorporate both temporal information and color information into the NLMeans algorithm, in order to restore video sequences. Starting from our algorithmic acceleration techniques, we investigate how the NLMeans algorithm can be easily mapped onto recent parallel computing architectures. In particular, we consider the graphical processing unit (GPU), which is available on most recent computers. Our developments lead to a high-quality denoising filter that can process DVD-resolution video sequences in real-time on a mid-range GPU.}},
author = {{Goossens, Bart and Luong, Hiep and Aelterman, Jan and Pizurica, Aleksandra and Philips, Wilfried}},
booktitle = {{Lecture Notes in Computer Science}},
editor = {{Blanc Talon, Jacques and Bone, Don and Philips, Wilfried and Popescu, Dan and Scheunders, Paul}},
isbn = {{9783642176906}},
issn = {{0302-9743}},
keywords = {{REMOVAL,NOISE,IMAGE,NONLOCAL MEANS,SCALE MIXTURES,WAVELET DOMAIN}},
language = {{eng}},
location = {{Sydney, Australia}},
pages = {{46--57}},
publisher = {{Springer}},
title = {{A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences}},
url = {{http://doi.org/10.1007/978-3-642-17691-3_5}},
volume = {{6475}},
year = {{2010}},
}
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