Analysis of single image super resolution models

Basit Öğe Kaydı
dc.authorid0000-0002-8944-5449
dc.authorid0000-0003-0298-0690
dc.contributor.authorKöprülü, Mertalien_US
dc.contributor.authorEskil, Mustafa Taneren_US
dc.date.accessioned2023-02-06T12:40:28Z
dc.date.available2023-02-06T12:40:28Z
dc.date.issued2022-11-18
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.description.abstractImage Super-Resolution (SR) is a set of image processing techniques which improve the resolution of images and videos. Deep learning approaches have made remarkable improvement in image super-resolution in recent years. This article aims and seeks to provide a comprehensive analysis on recent advances of models which has been used in image superresolution. This study has been investigated over other essential topics of current model problems, such as publicly accessible benchmark data-sets and performance evaluation measures. Finally, The study concluded these analysis by highlighting several weaknesses of existing base models as their feeding strategy and approved that the training technique which is Blind Feeding, which led several model to achieve state-of-the art.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationKöprülü, M. & Eskil. M. T. (2022). Analysis of single image super resolution models. Paper presented at the 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 1-6. doi:10.1109/ICECCME55909.2022.9988599en_US
dc.identifier.doi10.1109/ICECCME55909.2022.9988599
dc.identifier.endpage6
dc.identifier.isbn9781665470957
dc.identifier.isbn9781665470964
dc.identifier.scopus2-s2.0-85146434856
dc.identifier.scopusqualityN/A
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/5348
dc.identifier.urihttp://dx.doi.org/10.1109/ICECCME55909.2022.9988599
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKöprülü, Mertalien_US
dc.institutionauthorEskil, Mustafa Taneren_US
dc.institutionauthorid0000-0002-8944-5449
dc.institutionauthorid0000-0003-0298-0690
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEEen_US
dc.relation.ispartof2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural networken_US
dc.subjectGenerative adversarial networksen_US
dc.subjectImage processingen_US
dc.subjectSingle image super resolutionen_US
dc.subjectBenchmarkingen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep learningen_US
dc.subjectImage analysisen_US
dc.subjectImage enhancementen_US
dc.subjectOptical resolving poweren_US
dc.subjectComprehensive analysisen_US
dc.subjectCurrent modelingen_US
dc.subjectImage processing techniqueen_US
dc.subjectImage super resolutionsen_US
dc.subjectImages processingen_US
dc.subjectLearning approachen_US
dc.subjectSingle imagesen_US
dc.subjectSuper-resolution modelsen_US
dc.titleAnalysis of single image super resolution modelsen_US
dc.typeConference Objecten_US

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Bildiri Koleksiyonu | Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
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