Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/215
DC FieldValueLanguage
dc.contributor.authorLiu, Xuetingen_US
dc.contributor.otherXia, M.-
dc.contributor.otherWong, T. T.-
dc.date.accessioned2021-03-15T08:50:33Z-
dc.date.available2021-03-15T08:50:33Z-
dc.date.issued2018-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/215-
dc.description.abstractOnce a color image is converted to grayscale, it is a common belief that the original color cannot be fully restored, even with the state-of-the-art colorization methods. In this paper, we propose an innovative method to synthesize invertible grayscale. It is a grayscale image that can fully restore its original color. The key idea here is to encode the original color information into the synthesized grayscale, in a way that users cannot recognize any anomalies. We propose to learn and embed the color-encoding scheme via a convolutional neural network (CNN). It consists of an encoding network to convert a color image to grayscale, and a decoding network to invert the grayscale to color. We then design a loss function to ensure the trained network possesses three required properties: (a) color invertibility, (b) grayscale conformity, and (c) resistance to quantization error. We have conducted intensive quantitative experiments and user studies over a large amount of color images to validate the proposed method. Regardless of the genre and content of the color input, convincing results are obtained in all cases.en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.ispartofACM Transactions on Graphicsen_US
dc.titleInvertible grayscaleen_US
dc.typejournal articleen_US
dc.identifier.doi10.1145/3272127.3275080-
dc.contributor.affiliationSchool of Computing and Information Sciencesen_US
dc.relation.issn1557-7368en_US
dc.description.volume37en_US
dc.description.issue6en_US
dc.description.startpage1en_US
dc.description.endpage10en_US
dc.cihe.affiliatedNo-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.openairetypejournal article-
item.grantfulltextopen-
crisitem.author.deptSchool of Computing and Information Sciences-
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