A Robust and Fast Non-Local Means Algorithm for Image Denoising

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-l...

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Bibliographic Details
Published in:Journal of computer science and technology 2008-03, Vol.23 (2), p.270-279
Main Authors: Liu, Yan-Li, Wang, Jin, Chen, Xi, Guo, Yan-Wen, Peng, Qun-Sheng
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Breaking
Computation
Computer Science
Data Structures and Information Theory
Decomposition
Fast Fourier transformations
Fourier transforms
Information Systems Applications (incl.Internet)
Noise reduction
Pyramids
Redundancy
Regular Paper
Robustness
Similarity
Software Engineering
Theory of Computation
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Summary: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.
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-008-9129-8