Adaptive edge-preserving image denoising using wavelet transforms

dc.creatorda Silva, RD
dc.creatorMinetto, R
dc.creatorSchwartz, WR
dc.creatorPedrini, H
dc.date2013
dc.dateNOV
dc.date2014-08-01T18:17:27Z
dc.date2015-11-26T17:02:08Z
dc.date2014-08-01T18:17:27Z
dc.date2015-11-26T17:02:08Z
dc.date.accessioned2018-03-28T23:50:10Z
dc.date.available2018-03-28T23:50:10Z
dc.identifierPattern Analysis And Applications. Springer, v. 16, n. 4, n. 567, n. 580, 2013.
dc.identifier1433-7541
dc.identifier1433-755X
dc.identifierWOS:000325777300007
dc.identifier10.1007/s10044-012-0266-x
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/76689
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/76689
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1278856
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionImage denoising is a relevant issue found in diverse image processing and computer vision problems. It is a challenge to preserve important features, such as edges, corners and other sharp structures, during the denoising process. Wavelet transforms have been widely used for image denoising since they provide a suitable basis for separating noisy signal from the image signal. This paper describes a novel image denoising method based on wavelet transforms to preserve edges. The decomposition is performed by dividing the image into a set of blocks and transforming the data into the wavelet domain. An adaptive thresholding scheme based on edge strength is used to effectively reduce noise while preserving important features of the original image. Experimental results, compared to other approaches, demonstrate that the proposed method is suitable for different classes of images contaminated by Gaussian noise.
dc.description16
dc.description4
dc.description567
dc.description580
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFAPESP [2010/10618-3]
dc.languageen
dc.publisherSpringer
dc.publisherNew York
dc.publisherEUA
dc.relationPattern Analysis And Applications
dc.relationPattern Anal. Appl.
dc.rightsfechado
dc.rightshttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dc.sourceWeb of Science
dc.subjectImage denoising
dc.subjectWavelet transforms
dc.subjectAdaptive denoising
dc.subjectEdge preservation
dc.subjectNoise Removal
dc.subjectCross-validation
dc.subjectImpulse Noise
dc.subjectShrinkage
dc.subjectEfficient
dc.subjectDomain
dc.subjectCompression
dc.subjectSignal
dc.subjectCoefficients
dc.subjectAlgorithm
dc.titleAdaptive edge-preserving image denoising using wavelet transforms
dc.typeArtículos de revistas


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