A comparison of image fusion quality metrics

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dc.contributor.author Bendeş, Emre
dc.date.accessioned 2022-05-13T09:00:11Z
dc.date.available 2022-05-13T09:00:11Z
dc.date.issued 2021-12-15
dc.identifier.uri http://hdl.handle.net/20.500.11787/6568
dc.description.abstract Measuring the fitness of the fused image plays a key role in image fusion applications. For a learning process performed in a machine learning algorithm, the result of the fusion should be evaluated numerically. In the literature, there are well-known quality metrics developed for this purpose. Each metric evaluates the quality of the image using a different method. However, to be used in the learning process, the quality metrics must be able to provide results compatible with the change in the image's visual quality. In this study, synthetic images with known quality levels were created for this purpose. The scoring accuracy of six quality metrics commonly used in the literature was compared with these test images and the results were evaluated. tr_TR
dc.language.iso eng tr_TR
dc.publisher EurasianSciEnTech 2021 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Image fusion tr_TR
dc.subject Image quality metrics tr_TR
dc.subject Image processing tr_TR
dc.title A comparison of image fusion quality metrics tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal 3rd International Eurasian Conference on Science, Engineering and Technology (EurasianSciEnTech 2021) tr_TR
dc.contributor.department Nevşehir Hacı Bektaş Veli Üniversitesi/mühendislik-mimarlık fakültesi/bilgisayar mühendisliği bölümü/bilgisayar yazılımı anabilim dalı tr_TR
dc.contributor.authorID 30732 tr_TR


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