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Proceedings Paper

A fast non-local image denoising algorithm
Author(s): A. Dauwe; B. Goossens; H. Q. Luong; W. Philips
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Paper Abstract

In this paper we propose several improvements to the original non-local means algorithm introduced by Buades et al. which obtains state-of-the-art denoising results. The strength of this algorithm is to exploit the repetitive character of the image in order to denoise the image unlike conventional denoising algorithms, which typically operate in a local neighbourhood. Due to the enormous amount of weight computations, the original algorithm has a high computational cost. An improvement of image quality towards the original algorithm is to ignore the contributions from dissimilar windows. Even though their weights are very small at first sight, the new estimated pixel value can be severely biased due to the many small contributions. This bad influence of dissimilar windows can be eliminated by setting their corresponding weights to zero. Using the preclassification based on the first three statistical moments, only contributions from similar neighborhoods are computed. To decide whether a window is similar or dissimilar, we will derive thresholds for images corrupted with additive white Gaussian noise. Our accelerated approach is further optimized by taking advantage of the symmetry in the weights, which roughly halves the computation time, and by using a lookup table to speed up the weight computations. Compared to the original algorithm, our proposed method produces images with increased PSNR and better visual performance in less computation time. Our proposed method even outperforms state-of-the-art wavelet denoising techniques in both visual quality and PSNR values for images containing a lot of repetitive structures such as textures: the denoised images are much sharper and contain less artifacts. The proposed optimizations can also be applied in other image processing tasks which employ the concept of repetitive structures such as intra-frame super-resolution or detection of digital image forgery.

Paper Details

Date Published: 3 March 2008
PDF: 8 pages
Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 681210 (3 March 2008); doi: 10.1117/12.765505
Show Author Affiliations
A. Dauwe, Ghent Univ. (Belgium)
B. Goossens, Ghent Univ. (Belgium)
H. Q. Luong, Ghent Univ. (Belgium)
W. Philips, Ghent Univ. (Belgium)

Published in SPIE Proceedings Vol. 6812:
Image Processing: Algorithms and Systems VI
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)

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