Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/11779
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Akın, S.E. | - |
dc.contributor.author | Akgün, T. | - |
dc.date.accessioned | 2024-09-22T13:30:28Z | - |
dc.date.available | 2024-09-22T13:30:28Z | - |
dc.date.issued | 2024 | - |
dc.identifier.isbn | 979-835038896-1 | - |
dc.identifier.uri | https://doi.org/10.1109/SIU61531.2024.10600790 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/11779 | - |
dc.description | Berdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus University | en_US |
dc.description | 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 -- 15 May 2024 through 18 May 2024 -- Mersin -- 201235 | en_US |
dc.description.abstract | Infrared (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.iso | tr | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | autoencoder | en_US |
dc.subject | CNN | en_US |
dc.subject | colorization | en_US |
dc.subject | infrared | en_US |
dc.subject | thermal | en_US |
dc.subject | Infrared imaging | en_US |
dc.subject | Network architecture | en_US |
dc.subject | Auto encoders | en_US |
dc.subject | Colorization | en_US |
dc.subject | Image colorizations | en_US |
dc.subject | Infra-red sensor | en_US |
dc.subject | Infrared imaging sensors | en_US |
dc.subject | Infrared sensor | en_US |
dc.subject | Near Infrared | en_US |
dc.subject | Near-infrared | en_US |
dc.subject | Performance | en_US |
dc.subject | Thermal | en_US |
dc.subject | Deep neural networks | en_US |
dc.title | The Effects of Infrared Sensor Wavelength on Panchromatic Image Colorization Performance | en_US |
dc.title.alternative | Kızılötesi Algılayıcı Dalga Boyunun Pankromatik Görüntü Renklendirme Başarımı Üzerindeki Etkisi | en_US |
dc.type | Conference Object | en_US |
dc.department | TOBB ETÜ | en_US |
dc.identifier.scopus | 2-s2.0-85200847047 | en_US |
dc.institutionauthor | … | - |
dc.identifier.doi | 10.1109/SIU61531.2024.10600790 | - |
dc.authorscopusid | 59254147300 | - |
dc.authorscopusid | 9273895500 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
item.openairetype | Conference Object | - |
item.languageiso639-1 | tr | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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