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dc.contributor.authorLi, Pengliang
dc.contributor.authorLiang, Junli
dc.contributor.authorZhang, Miaohua
dc.contributor.authorFan, Wen
dc.contributor.authorYu, Guoyang
dc.date.accessioned2022-01-13T01:57:12Z
dc.date.available2022-01-13T01:57:12Z
dc.date.issued2021
dc.identifier.issn0031-3203en_US
dc.identifier.doi10.1016/j.patcog.2021.108506en_US
dc.identifier.urihttp://hdl.handle.net/10072/411406
dc.description.abstractJoint image denoising algorithms use the structures of the guidance image as a prior to restore the noisy target image. While the provided guidance images are helpful to improve the denoising performance, the denoised edges are most likely to be blurred especially when the edges of the guidance image are weak or inexistent. To address this weakness, this paper proposes a new gradient-direction-based joint image denoising method in which the absolute cosine value of the angle between two gradient vectors of the guidance image and those of the image to recover is employed as the parallel measurement to ensure that the gradient directions of the denoised image are approximately the same as or opposite to those of the guidance image. Besides, a new edge-preserving regularization term is developed to alleviate the effects of the unreliable prior information from guidance image. To simplify the resultant complex nonconvex and nonlinear fractional model, the logarithm function is employed to convert the multiplication operation into addition operation. Then, we construct the surrogate function for the logarithmic term ofen_US
dc.languageenen_US
dc.publisherElsevier BVen_US
dc.relation.ispartofpagefrom108506en_US
dc.relation.ispartofjournalPattern Recognitionen_US
dc.titleJoint Image Denoising with Gradient Direction and Edge-Preserving Regularizationen_US
dc.typeJournal articleen_US
dcterms.bibliographicCitationLi, P; Liang, J; Zhang, M; Fan, W; Yu, G, Joint Image Denoising with Gradient Direction and Edge-Preserving Regularization, Pattern Recognition, 2021, pp. 108506en_US
dcterms.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.date.updated2022-01-05T04:34:35Z
dc.description.versionAccepted Manuscript (AM)en_US
gro.description.notepublicThis publication has been entered as an advanced online version in Griffith Research Online.en_US
gro.rights.copyright© 2021 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.en_US
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gro.griffith.authorZhang, Lena


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