Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/2319
DC FieldValueLanguage
dc.contributor.authorSiu, Wan Chien_US
dc.contributor.otherHuang, J.-J.-
dc.date.accessioned2022-02-18T04:22:42Z-
dc.date.available2022-02-18T04:22:42Z-
dc.date.issued2015-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/2319-
dc.description.abstractIn this paper, a novel learning-based single image super-resolution method using random forest is proposed. Different from example-based super-resolution methods which search for similar image patches from an external database or the input image, and the sparse representation model based methods which rely on the sparse representation, this proposed super-resolution with random forest (SRRF) method takes the divide-and-conquer strategy. Random forest is applied to classify the training LR-HR patch pairs into a number of classes. Within every class, a simple linear regression model is used to model the relationship between the LR image patches and their corresponding HR image patches. Experimental results show that the proposed SRRF method can generate the state-of-the-art super-resolved images with near real-time performance.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.titlePractical application of random forests for super-resolution imagingen_US
dc.typeconference proceedingsen_US
dc.relation.publicationProceedings of the 2015 IEEE International Symposium on Circuits and Systems (ISCAS)en_US
dc.identifier.doi10.1109/ISCAS.2015.7169108-
dc.contributor.affiliationSchool of Computing and Information Sciencesen_US
dc.relation.isbn9781479983926en_US
dc.description.startpage2161en_US
dc.description.endpage2164en_US
dc.cihe.affiliatedNo-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.cerifentitytypePublications-
item.openairetypeconference proceedings-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
crisitem.author.deptSchool of Computing and Information Sciences-
crisitem.author.orcid0000-0001-8280-0367-
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