Abstract
In the paper, we propose a robust and fast image denoising method. The approach integrates both Non-Local means algorithm and Laplacian Pyramid. Given an image to be denoised, we first decompose it into Laplacian pyramid. Exploiting the redundancy property of Laplacian pyramid, we then perform non-local means on every level image of Laplacian pyramid. Essentially, we use the similarity of image features in Laplacian pyramid to act as weight to denoise image. Since the features extracted in Laplacian pyramid are localized in spatial position and scale, they are much more able to describe image, and computing the similarity between them is more reasonable and more robust. Also, based on the efficient Summed Square Image (SSI) scheme and Fast Fourier Transform (FFT), we present an accelerating algorithm to break the bottleneck of non-local means algorithm — similarity computation of compare windows. After speedup, our algorithm is fifty times faster than original non-local means algorithm. Experiments demonstrated the effectiveness of our algorithm.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Lindenbaum M, Fischer M, Bruckstein A M. On Gabor contribution to image enhancement. Pattern Recognition, 1994, 27(1): 1–8.
Alvarez L, Lions P L, Morel J M. Image selective smoothing and edge detection by nonlinear diffusion (ii). Journal of Numerical Analysis, 1992, 29(3): 845–866.
Yin L, Yang R, Gabbouj M, Neuvo Y. Weighted median filters: A tutorial. IEEE Trans. Circuits and Systems, 1996, 43(3): 157–192.
Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In Proc. the Sixth International Conference on Computer Vision, Bombay, India, 1998, pp.839–846.
Donoho D. De-noising by soft-thresholding. IEEE Trans. Information Theory, 1995, 41(3): 613–627.
Chambolle A, DeVore R A, Lee N Y, Lucier B J. Nonlinear wavelet image processing: Variational problems, compression, and noise removal through wavelet shrinkage. IEEE Trans. Image Processing, 1998, 7(1): 319–335.
Cohen I, Raz S, Malah D. Translation invariant denoising using the minimum description length criterion. Signal Processing, 1999, 75(3): 201–223.
Portilla J, Strela V, Wainwright M J, Simoncelli E P. Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans. Image Processing, 2003, 12(11): 1338–1351.
Romberg J, Choi H, Baraniuk R G. Bayesian tree-structured wavelet-domain image modeling using hidden Markov models. IEEE Trans. Image Processing, 2001, 10(7): 1056–1068.
Buades A, Coll B, Morel J M. A non-local algorithm for image denoising. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, USA, 2005, Vol.2, pp.60–65.
Ahmadian A, Bharath A A. Orthogonal wavelets for image transmission and compression schemes: Implementation and results. In Proc. SPIE, 1996, 2825(2): 822–833.
Kharate G K, Ghatol A A, Rege P P. Image compression using wavelet packet tree based on threshold entropy. In Proc. the 24th IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, Innsbruck, Austria, 2006, pp.322–325.
Jansen M, Bultheel A. Multiple wavelet threshold estimation by generalized crossvalidation for images with correlated noise. IEEE Trans. Image Processing, 1999, 8(7): 947–953.
Aiazzi B, Alparone L, Baronti S, Borri G. Pyramid-based multiresolution adaptive filters for additive multiplicative image noise. IEEE Trans. Circuits Syst. II, 1998, 45(8): 1092–1097.
Burt P J, Adelson E H. The Laplacian pyramid as a compact image code. IEEE Trans. Communications, 1983, 31(4): 532–540.
Alexey L A. Multiresolution approach for improving quality of image denoising algorithms. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse, France, 2006.
Mahmoudi M, Sapiro G. Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Processing Letters, 2005, 12(12): 839–842.
Heeger D J, Bergen J R. Pyramid-based texture analysis/synthesis. In Proc. SIGGRAPH, Los Angeles, USA, 1995, pp.229–238.
Greenspan H, Goodman R, Chellappa R, Anderson C H. Learning texture discrimination rules in a multiresolution system. IEEE Trans. Pattern Analysis and Machine Intelligence, 1994, 16(9): 894–901.
Bouzidi A, Baaziz N. Contourlet domain feature extraction for image content authentication. In Proc. IEEE 8th Workshop on Multimedia Signal Processing, Victoria, Canada, 2006, pp.202–206.
Fuchs C. Extraktion polymorpher Bildstrukturen und ihre topologische und geometrische Gruppierung. DGK, Bayer. Akademie der Wissenschaften, Reihe C, Heft 502, 1998.
Viola P, Michael J. Rapid object detection using a boosted cascade of simple features. In Proc. IEEE CVPR, Hanoii, USA, 2001, pp.511–518
Author information
Authors and Affiliations
Corresponding author
Additional information
This work is supported by the National Grand Fundamental Research 973 Program of China (Grant No. 2002CB312101), the National Natural Science Foundation of China (Grant Nos. 60403038 and 60703084) and the Natural Science Foundation of Jiangsu Province (Grant No. BK2007571).
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Liu, YL., Wang, J., Chen, X. et al. A Robust and Fast Non-Local Means Algorithm for Image Denoising. J. Comput. Sci. Technol. 23, 270–279 (2008). https://doi.org/10.1007/s11390-008-9129-8
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11390-008-9129-8