Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11779
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAkın, S.E.-
dc.contributor.authorAkgün, T.-
dc.date.accessioned2024-09-22T13:30:28Z-
dc.date.available2024-09-22T13:30:28Z-
dc.date.issued2024-
dc.identifier.isbn979-835038896-1-
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10600790-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/11779-
dc.descriptionBerdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus Universityen_US
dc.description32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 -- 15 May 2024 through 18 May 2024 -- Mersin -- 201235en_US
dc.description.abstractInfrared (IR) imaging sensors, designed to detect the wavelength range between 0.9µm and 14µm, offer unique advantages over daylight cameras in consumer, industrial, and defense applications. However, IR images lack natural color information and can be challenging for individuals without sensor-specific training to interpret. Consequently, transforming IR images into perceptually realistic color images represents a valuable research endeavor with significant commercial potential. Recently, various studies utilizing deep neural networks for colorizing single-mode (near-IR or thermal) infrared images have been reported. This article will apply a common neural network architecture to images captured with different imaging modes (near-IR, thermal IR, and low-light) for colorization and compare the results. These experiments will examine the influence of perceived wavelength on the colorization process. © 2024 IEEE.en_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectautoencoderen_US
dc.subjectCNNen_US
dc.subjectcolorizationen_US
dc.subjectinfrareden_US
dc.subjectthermalen_US
dc.subjectInfrared imagingen_US
dc.subjectNetwork architectureen_US
dc.subjectAuto encodersen_US
dc.subjectColorizationen_US
dc.subjectImage colorizationsen_US
dc.subjectInfra-red sensoren_US
dc.subjectInfrared imaging sensorsen_US
dc.subjectInfrared sensoren_US
dc.subjectNear Infrareden_US
dc.subjectNear-infrareden_US
dc.subjectPerformanceen_US
dc.subjectThermalen_US
dc.subjectDeep neural networksen_US
dc.titleThe Effects of Infrared Sensor Wavelength on Panchromatic Image Colorization Performanceen_US
dc.title.alternativeKızılötesi Algılayıcı Dalga Boyunun Pankromatik Görüntü Renklendirme Başarımı Üzerindeki Etkisien_US
dc.typeConference Objecten_US
dc.departmentTOBB ETÜen_US
dc.identifier.scopus2-s2.0-85200847047en_US
dc.institutionauthor-
dc.identifier.doi10.1109/SIU61531.2024.10600790-
dc.authorscopusid59254147300-
dc.authorscopusid9273895500-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1tr-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Show simple item record



CORE Recommender

Page view(s)

34
checked on Dec 23, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.