Fast nonlocal filtering applied to electron cryomicroscopy

Darbon, Jérôme; Cunha, Alexandre; Chan, Tony F.; Osher, Stanley; Jensen, Grant J.

doi:10.1109/isbi.2008.4541250

Cited by 202 publications

(175 citation statements)

References 9 publications

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“…A pseudo code of the described implementation is given in Algorithm 1. Specifically speaking, the integral image (II) [8], [9] (line 3) is a fast algorithm for computing arbitrary rectangular sums. In particular, it requires 2 operations/pixel to construct an II and 3 operations/pixel to extract the sum of pixels within a rectangular region.…”

Section: Methodsmentioning

confidence: 99%

“…Because integral images are used, the complexity of each shrinkage round is independent of the used block size. For a 256×256 grayscale image, the time complexity of executing the fast NLM [9], the SURE computation, and the additional BSS/round (from 300 realizations with 2.4GHz cpu) are .4309±.0077, .0181±.0008, and .0177±.0016 seconds, respectively.…”

Section: : End Whilementioning

confidence: 99%

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Fast blockwise SURE shrinkage for image denoising

Tracey

Natarajan

et al. 2014

Signal Processing

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In this letter, we investigate the shrinkage problem for the non-local means (NLM) image denoising.In particular, we derive the closed-form of the optimal blockwise shrinkage for NLM that minimizes the Stein's unbiased risk estimator (SURE). We also propose a constant complexity algorithm allowing fast blockwise shrinkage. Simulation results show that the proposed blockwise shrinkage method improves NLM performance in attaining higher peak signal noise ratio (PSNR) and structural similarity index Index TermsImage Denoising, Non-Local Means, Adaptive Algorithm, Shrinkage Estimator, Stein's Unbiased Risk EstimatorBrian Tracey and Joseph P. Noonan are with the department of electrical and computer engineering, Tufts university, 161 College Ave, Medford, MA 02155; e-mail: ywu03@ece.tufts.edu. Premkumar Natarajan is with the Raytheon BBN technologies, 10 Moulton St., Cambridge, MA 02138. Yue Wu was with the department of electrical and computer engineering at Tufts university, but is now with the Raytheon BBN technologies.

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Section: Methodsmentioning

confidence: 99%

Section: : End Whilementioning

confidence: 99%

Fast blockwise SURE shrinkage for image denoising

Tracey

Natarajan

et al. 2014

Signal Processing

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“…In particular, it can be seen as a bilateral filter [75] with a patch-based photometric similarity measure. Several variational formulations of the NL-means filter have been proposed [47,38,5,13,52] together with acceleration techniques [55,8,26,28,13,60] and invariant patch similarity measures [79,48,91,53]. This method has inspired the appearance of numerous so-called patch-based approaches for image smoothing, deblurring, segmentation, inpainting, super-resolution, and texture synthesis, among others.…”

Section: Introductionmentioning

confidence: 99%

Generalised Nonlocal Image Smoothing

Pizarro

Pavel²,

Didas

et al. 2010

Int J Comput Vis

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We propose a discrete variational approach for image smoothing consisting of nonlocal data and smoothness contraints that penalise general dissimilarity measures defined on image patches. One of such dissimilarity measures is the weighted 2 distance between patches. In such a case we derive an iterative neighbourhood filter that induces a new similarity measure in the photometric domain. It can be regarded as an extended patch similarity measure that evaluates not only the patch similarity of two chosen pixels, but also the similarity of their corresponding neighbours. This leads to a more robust smoothing process since the pixels selected for averaging are more coherent with the local image structure. The suggested approach includes two recently proposed filters as special cases: The NLmeans filter of Buades et al. and the NDS filter of Mrázek et al. In fact, the approach introduced here can be considered as a generalisation of the latter filter. We evaluate our method for the task of denoising greyscale and colour images degraded by Gaussian and impulse noise, demonstrating that it compares very well to other more sophisticated patch-based approaches.

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“…Em algumas implementações ou variações do algoritmo NLM, é desprezado sem grande influência nos resultados (DARBON, 2008;ZHONG, 2012).…”

Section: Métodos De Filtragem De Ruídounclassified

“…Figura 46 -Gráfico do NAQI das imagens mamográficas clínicas SIM05 (Banco III), onde a linha preta ilustra os valores para as imagens adquiridas com 100% da dose padrão de radiação e a linha cinza são os valores para as imagens com doses reduzidas, antes da filtragem do ruído. (DARBON, 2008;DELEDALLE, 2010;SALMON, 2010;ZHONG, 2012;BINDILATTI, 2013 …”

Section: Introductionunclassified

Método baseado em médias não-locais para filtragem do ruído quântico de imagens mamográficas digitais adquiridas com dose de radiação reduzida

Nunes¹

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Fast nonlocal filtering applied to electron cryomicroscopy

Cited by 202 publications

References 9 publications

Fast blockwise SURE shrinkage for image denoising

Fast blockwise SURE shrinkage for image denoising

Generalised Nonlocal Image Smoothing

Método baseado em médias não-locais para filtragem do ruído quântico de imagens mamográficas digitais adquiridas com dose de radiação reduzida

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