Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91303
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLi, M-
dc.creatorHe, X-
dc.creatorLam, KM-
dc.creatorZhang, K-
dc.creatorJing, J-
dc.date.accessioned2021-11-02T08:22:11Z-
dc.date.available2021-11-02T08:22:11Z-
dc.identifier.issn1751-9659-
dc.identifier.urihttp://hdl.handle.net/10397/91303-
dc.language.isoenen_US
dc.publisherInstitution of Engineering and Technologyen_US
dc.rights© 2021 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technologyen_US
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Li, M., He, X., Lam, K. M., Zhang, K., & Jing, J. (2021). Face hallucination based on cluster consistent dictionary learning. IET Image Processing is available at https://doi.org/10.1049/ipr2.12269en_US
dc.titleFace hallucination based on cluster consistent dictionary learningen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2841-
dc.identifier.epage2853-
dc.identifier.volume15-
dc.identifier.issue12-
dc.identifier.doi10.1049/ipr2.12269-
dcterms.abstractFace hallucination is a super-resolution technique specially designed to reconstruct high-resolution faces from low-resolution faces. Most state-of-the-art algorithms leverage position-patch prior knowledge of human faces to better super-resolve face images. However, most of them assume the training face dataset is sufficiently large, well cropped or aligned. This paper, proposes a novel example-based face hallucination method, based on cluster consistent dictionary learning with the assumption that human faces have similar facial structures. In this method, the paired face image patches are firstly labelled as face areas including eyes, nose, mouth and other parts, as well as non-face areas without requiring the training face images cropped and aligned. Then, the training patches are clustered according their labels and textures. The cluster consistent dictionary is learned to represent the low-resolution patches and the high-resolution patches. Finally, the high-resolution patches of the input low-resolution face image can be efficiently generated by using the adjusted anchored neighbourhood regression. As utilizing the labelled facial parts prior knowledge, the proposed method represents more details in the reconstruction. Experimental results demonstrate that the authors' algorithm outperforms many state-of-the-art techniques for face hallucination under different datasets.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIET image processing, Oct. 2021, v. 15, no. 12, p. 2841-2853-
dcterms.isPartOfIET image processing-
dcterms.issued2021-10-
dc.identifier.scopus2-s2.0-85106289149-
dc.identifier.eissn1751-9667-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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