Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/606516
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dc.contributor.authorRuiqin Xiongen_US
dc.contributor.authorHangfan Liuen_US
dc.contributor.authorXinfeng Zhangen_US
dc.contributor.authorJian Zhangen_US
dc.contributor.authorSiwei Maen_US
dc.contributor.authorFeng Wuen_US
dc.contributor.authorWen Gaoen_US
dc.date.accessioned2020-05-20T09:03:11Z-
dc.date.available2020-05-20T09:03:11Z-
dc.date.issued2016en_US
dc.identifier.issn1057-7149en_US
dc.identifier.issn1941-0042en_US
dc.identifier.other10.1109/TIP.2016.2614160en_US
dc.identifier.urihttp://localhost/handle/Hannan/137853en_US
dc.identifier.urihttp://localhost/handle/Hannan/606516-
dc.description.abstractThis paper proposes a new image denoising algorithm based on adaptive signal modeling and regularization. It improves the quality of images by regularizing each image patch using bandwise distribution modeling in transform domain. Instead of using a global model for all the patches in an image, it employs content-dependent adaptive models to address the non-stationarity of image signals and also the diversity among different transform bands. The distribution model is adaptively estimated for each patch individually. It varies from one patch location to another and also varies for different bands. In particular, we consider the estimated distribution to have non-zero expectation. To estimate the expectation and variance parameters for every band of a particular patch, we exploit the nonlocal correlation in image to collect a set of highly similar patches as the data samples to form the distribution. Irrelevant patches are excluded so that such adaptively learned model is more accurate than a global one. The image is ultimately restored via bandwise adaptive soft-thresholding, based on a Laplacian approximation of the distribution of similar-patch group transform coefficients. Experimental results demonstrate that the proposed scheme outperforms several state-of-the-art denoising methods in both the objective and the perceptual qualities.en_US
dc.publisherIEEEen_US
dc.relation.haspart7577781.pdfen_US
dc.subjectnonlocal similarity|adaptive regularization|bandwise modeling|Image denoising|transform domain modeling|adaptive soft-thresholdingen_US
dc.titleImage Denoising via Bandwise Adaptive Modeling and Regularization Exploiting Nonlocal Similarityen_US
dc.typeArticleen_US
dc.journal.volume25en_US
dc.journal.issue12en_US
dc.journal.titleIEEE Transactions on Image Processingen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorRuiqin Xiongen_US
dc.contributor.authorHangfan Liuen_US
dc.contributor.authorXinfeng Zhangen_US
dc.contributor.authorJian Zhangen_US
dc.contributor.authorSiwei Maen_US
dc.contributor.authorFeng Wuen_US
dc.contributor.authorWen Gaoen_US
dc.date.accessioned2020-05-20T09:03:11Z-
dc.date.available2020-05-20T09:03:11Z-
dc.date.issued2016en_US
dc.identifier.issn1057-7149en_US
dc.identifier.issn1941-0042en_US
dc.identifier.other10.1109/TIP.2016.2614160en_US
dc.identifier.urihttp://localhost/handle/Hannan/137853en_US
dc.identifier.urihttp://localhost/handle/Hannan/606516-
dc.description.abstractThis paper proposes a new image denoising algorithm based on adaptive signal modeling and regularization. It improves the quality of images by regularizing each image patch using bandwise distribution modeling in transform domain. Instead of using a global model for all the patches in an image, it employs content-dependent adaptive models to address the non-stationarity of image signals and also the diversity among different transform bands. The distribution model is adaptively estimated for each patch individually. It varies from one patch location to another and also varies for different bands. In particular, we consider the estimated distribution to have non-zero expectation. To estimate the expectation and variance parameters for every band of a particular patch, we exploit the nonlocal correlation in image to collect a set of highly similar patches as the data samples to form the distribution. Irrelevant patches are excluded so that such adaptively learned model is more accurate than a global one. The image is ultimately restored via bandwise adaptive soft-thresholding, based on a Laplacian approximation of the distribution of similar-patch group transform coefficients. Experimental results demonstrate that the proposed scheme outperforms several state-of-the-art denoising methods in both the objective and the perceptual qualities.en_US
dc.publisherIEEEen_US
dc.relation.haspart7577781.pdfen_US
dc.subjectnonlocal similarity|adaptive regularization|bandwise modeling|Image denoising|transform domain modeling|adaptive soft-thresholdingen_US
dc.titleImage Denoising via Bandwise Adaptive Modeling and Regularization Exploiting Nonlocal Similarityen_US
dc.typeArticleen_US
dc.journal.volume25en_US
dc.journal.issue12en_US
dc.journal.titleIEEE Transactions on Image Processingen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7577781.pdf7.06 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRuiqin Xiongen_US
dc.contributor.authorHangfan Liuen_US
dc.contributor.authorXinfeng Zhangen_US
dc.contributor.authorJian Zhangen_US
dc.contributor.authorSiwei Maen_US
dc.contributor.authorFeng Wuen_US
dc.contributor.authorWen Gaoen_US
dc.date.accessioned2020-05-20T09:03:11Z-
dc.date.available2020-05-20T09:03:11Z-
dc.date.issued2016en_US
dc.identifier.issn1057-7149en_US
dc.identifier.issn1941-0042en_US
dc.identifier.other10.1109/TIP.2016.2614160en_US
dc.identifier.urihttp://localhost/handle/Hannan/137853en_US
dc.identifier.urihttp://localhost/handle/Hannan/606516-
dc.description.abstractThis paper proposes a new image denoising algorithm based on adaptive signal modeling and regularization. It improves the quality of images by regularizing each image patch using bandwise distribution modeling in transform domain. Instead of using a global model for all the patches in an image, it employs content-dependent adaptive models to address the non-stationarity of image signals and also the diversity among different transform bands. The distribution model is adaptively estimated for each patch individually. It varies from one patch location to another and also varies for different bands. In particular, we consider the estimated distribution to have non-zero expectation. To estimate the expectation and variance parameters for every band of a particular patch, we exploit the nonlocal correlation in image to collect a set of highly similar patches as the data samples to form the distribution. Irrelevant patches are excluded so that such adaptively learned model is more accurate than a global one. The image is ultimately restored via bandwise adaptive soft-thresholding, based on a Laplacian approximation of the distribution of similar-patch group transform coefficients. Experimental results demonstrate that the proposed scheme outperforms several state-of-the-art denoising methods in both the objective and the perceptual qualities.en_US
dc.publisherIEEEen_US
dc.relation.haspart7577781.pdfen_US
dc.subjectnonlocal similarity|adaptive regularization|bandwise modeling|Image denoising|transform domain modeling|adaptive soft-thresholdingen_US
dc.titleImage Denoising via Bandwise Adaptive Modeling and Regularization Exploiting Nonlocal Similarityen_US
dc.typeArticleen_US
dc.journal.volume25en_US
dc.journal.issue12en_US
dc.journal.titleIEEE Transactions on Image Processingen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7577781.pdf7.06 MBAdobe PDFThumbnail
Preview File