2006 International Conference on Image Processing 2006
DOI: 10.1109/icip.2006.312698
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Fast Non-Local Algorithm for Image Denoising

Abstract: For the non-local denoising approach presented by Buades et al., remarkable denoising results are obtained at high expense of computational cost. In this paper, a new algorithm that reduces the computational cost for calculating the similarity of neighborhood windows is proposed. We first introduce an approximate measure about the similarity of neighborhood windows, then we use an efficient Summed Square Image (SSI) scheme and Fast Fourier Transform (FFT) to accelerate the calculation of this measure. Our algo… Show more

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Cited by 152 publications
(100 citation statements)
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“…The objective of denoising is to remove the noise effectively while preserving the original image details as much as possible. So far, many approaches have been proposed to get rid of noise [12]. Traditionally, this is achieved by linear processing such as Wiener filtering [13,[14][15].…”
Section: Introductionmentioning
confidence: 99%
“…The objective of denoising is to remove the noise effectively while preserving the original image details as much as possible. So far, many approaches have been proposed to get rid of noise [12]. Traditionally, this is achieved by linear processing such as Wiener filtering [13,[14][15].…”
Section: Introductionmentioning
confidence: 99%
“…A strategy for reducing the complexity of patch distance computations while maintaining exact calculations is described in [25] and [7]. The method is known as integral images [24] (or summed area tables [6] in the context of texture mapping) and it allows to efficiently compute the sum of values of any image in a rectangular subset of a grid.…”
Section: Fast Nlm-p Using Integral Imagesmentioning
confidence: 99%
“…Denoising methods on spatial domain cannot remove plenty of noise information but the image transform domain can often work wonders. Fourier Transforms in signal processing is one of the most important and widely used transform [15]. For two-dimensional discrete-time signal ( , ), = 0, 1, … , − 1, = 0, 1, … , − 1,, whose Fourier transform definitions are as follows:…”
Section: Fourier Transformmentioning
confidence: 99%