Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/1262
DC FieldValueLanguage
dc.contributor.authorSiu, Wan Chien_US
dc.contributor.authorLiu, Zhisong-
dc.contributor.otherChan, Y.-L.-
dc.date.accessioned2021-08-12T07:03:28Z-
dc.date.available2021-08-12T07:03:28Z-
dc.date.issued2018-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/1262-
dc.description.abstractBenefiting from the great power of graphic processor units, researchers can now come up with more and more sophisticated and complex deep learning structures to solve computer vision problems in various fields with excellent results. However, real-time performance is the bottleneck for deep learning in some applications, like image super-resolution. In this paper, we propose an image super-resolution making use of both the advantages of Back Projection and Residual Networks (BPRN). It generalizes the residual networks as a hierarchical back projection process. We use both convolution and deconvolution to down- and up-sample images to feedback the residues for super-resolution. Furthermore, we come up with a Lighter BPRN (L-BPRN) model to achieve similar state-of-the-art PSNR but fewer network parameters. The testing process is much faster and also accurate for image super-resolution with different scaling factors. Compared with recent deep learning based image super-resolution approaches, experimental results show that our proposed methods can achieve the state-of-the-art PSNR and SSIM performance as well as fast realization.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.titleJoint Back Projection and Residual Networks for efficient image super-resolutionen_US
dc.typeconference proceedingsen_US
dc.relation.publication2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Proceedingsen_US
dc.identifier.doi10.23919/APSIPA.2018.8659476-
dc.contributor.affiliationSchool of Computing and Information Sciencesen_US
dc.relation.isbn9781728102436en_US
dc.description.startpage1054en_US
dc.description.endpage1060en_US
dc.cihe.affiliatedNo-
item.openairetypeconference proceedings-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.grantfulltextnone-
item.cerifentitytypePublications-
crisitem.author.deptSchool of Computing and Information Sciences-
crisitem.author.deptSchool of Computing and Information Sciences-
crisitem.author.orcid0000-0001-8280-0367-
crisitem.author.orcid0000-0003-4507-3097-
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