Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/81662
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | - |
dc.creator | Liu, ZS | - |
dc.creator | Siu, WC | - |
dc.creator | Chan, YL | - |
dc.date.accessioned | 2020-02-10T12:28:29Z | - |
dc.date.available | 2020-02-10T12:28:29Z | - |
dc.identifier.issn | 2169-3536 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/81662 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0 | en_US |
dc.rights | The following publication Z. Liu, W. Siu and Y. Chan, "Reference Based Face Super-Resolution," in IEEE Access, vol. 7, pp. 129112-129126, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2934078 | en_US |
dc.subject | Face super-resolution | en_US |
dc.subject | Deep feature extraction | en_US |
dc.subject | Style transfer | en_US |
dc.title | Reference based face super-resolution | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 129112 | en_US |
dc.identifier.epage | 129126 | en_US |
dc.identifier.volume | 7 | en_US |
dc.identifier.doi | 10.1109/ACCESS.2019.2934078 | en_US |
dcterms.abstract | Despite the great progress of image super-resolution in recent years, face super-resolution has still much room to explore good visual quality while preserving original facial attributes for larger up-scaling factors. This paper investigates a new research direction in face super-resolution, called Reference based face Super-Resolution (RefSR), in which a reference facial image containing genuine attributes is provided in addition to the low-resolution images for super-resolution. We focus on transferring the key information extracted from reference facial images to the super-resolution process to guarantee the content similarity between the reference and super-resolution image. We propose a novel Conditional Variational AutoEncoder model for this Reference based Face Super-Resolution (RefSR-VAE). By using the encoder to map the reference image to the joint latent space, we can then use the decoder to sample the encoder results to super-resolve low-resolution facial images to generate super-resolution images with good visual quality. We create a benchmark dataset on reference based face super-resolution (RefSR-Face) for general research use, which contains reference images paired with low-resolution images of various pose, emotions, ages and appearance. Both objective and subjective evaluations were conducted, which demonstrate the great potential of using reference images for face super-resolution. By comparing it with state-of-the-art super-resolution approaches, our proposed approach also achieves superior performance. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, 2019, v. 7, p. 129112-129126 | en_US |
dcterms.isPartOf | IEEE access | en_US |
dcterms.issued | 2019 | - |
dc.identifier.isi | WOS:000487233800078 | - |
dc.identifier.scopus | 2-s2.0-85077975142 | - |
dc.description.validate | 202002 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Liu_Reference_Face_Super-Resolution.pdf | 3.22 MB | Adobe PDF | View/Open |
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