{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:28:07Z","timestamp":1740122887091,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Scientific Research Project Fund of Konya Technical University","award":["201102001"],"award-info":[{"award-number":["201102001"]}]},{"DOI":"10.13039\/501100004410","name":"Scientific and Technological Research Council of Turkey","doi-asserted-by":"crossref","award":["215E019"],"award-info":[{"award-number":["215E019"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s11042-022-14169-0","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T09:03:13Z","timestamp":1668675793000},"page":"18483-18500","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Cloud environment-based super resolution application for thermal images using the new approach TSRGAN+ model"],"prefix":"10.1007","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7831-6724","authenticated-orcid":false,"given":"Fatih Mehmet","family":"Senalp","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8878-8506","authenticated-orcid":false,"given":"Batuhan","family":"Orhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6503-9668","authenticated-orcid":false,"given":"Murat","family":"Ceylan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"issue":"1","key":"14169_CR1","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1108\/IJIUS-01-2019-0005","volume":"8","author":"SDM Achanta","year":"2020","unstructured":"Achanta SDM, Karthikeyan T, Vinothkanna R (2020) A wireless IOT system towards gait detection technique using FSR sensor and wearable IOT devices. Int J Intell Unmanned Syst 8(1):43\u201354. https:\/\/doi.org\/10.1108\/IJIUS-01-2019-0005","journal-title":"Int J Intell Unmanned Syst"},{"key":"14169_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3390462","volume":"53","author":"S Anwar","year":"2020","unstructured":"Anwar S, Khan S, Barnes N (2020) A deep journey into super-resolution: a survey. ACM Comput Surv 53:1\u201334. https:\/\/doi.org\/10.1145\/3390462","journal-title":"ACM Comput Surv"},{"key":"14169_CR3","doi-asserted-by":"publisher","unstructured":"Choi Y, Kim N, Hwang S, Kweon IS (2016) Thermal image enhancement using convolutional neural network. In: IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 223\u2013230. https:\/\/doi.org\/10.1109\/IROS.2016.7759059","DOI":"10.1109\/IROS.2016.7759059"},{"key":"14169_CR4","doi-asserted-by":"publisher","unstructured":"Chudasama V, Patel H, Prajapati K, Upla K, Ramachandra R, Raja K, Busch C (2020) TherISuRNet- A computationally efficient thermal image super-resolution network. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 388\u2013397. https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00051","DOI":"10.1109\/CVPRW50498.2020.00051"},{"key":"14169_CR5","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong C, Loy CC, He K, Tan X (2015) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38:295\u2013307. https:\/\/doi.org\/10.1109\/TPAMI.2015.2439281","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"14169_CR6","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1109\/TIP.2016.2542360","volume":"25","author":"W Dong","year":"2016","unstructured":"Dong W, Fu F, Shi G, Cao X, Wu J, Li G, Li X (2016) Hyperspectral image super-resolution via non-negative structured sparse representation. IEEE Trans Image Process 25(5):2337\u20132352. https:\/\/doi.org\/10.1109\/TIP.2016.2542360","journal-title":"IEEE Trans Image Process"},{"key":"14169_CR7","doi-asserted-by":"publisher","unstructured":"Dong C, Loy CC, Tang X (2016) Accelerating the Super-Resolution Convolutional Neural Network. Computer Vision ECCV 2016 Lecture Notes in Comput Sci Springer 9906. https:\/\/doi.org\/10.1007\/978-3-319-46475-6_25","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"14169_CR8","doi-asserted-by":"publisher","unstructured":"Dosovitskiy A, Brox T (2016) Generating images with perceptual similarity metrics based on deep networks. In advances in neural information processing systems (NIPS). Pp 658\u2013666. https:\/\/doi.org\/10.48550\/arXiv.1602.02644","DOI":"10.48550\/arXiv.1602.02644"},{"key":"14169_CR9","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.neucom.2017.07.017","volume":"272","author":"Z Fan","year":"2018","unstructured":"Fan Z, Bi D, Xiong L, Ma S, He L, Ding W (2018) Dim infrared image enhancement based on convolutional neural network. Neurocomputing 272:396\u2013404. https:\/\/doi.org\/10.1016\/j.neucom.2017.07.017","journal-title":"Neurocomputing"},{"issue":"1","key":"14169_CR10","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1186\/s12938-017-0408-x","volume":"16","author":"L Fraiwan","year":"2017","unstructured":"Fraiwan L, Alkhodari M, Ninan J, Mustafa B, Saleh A, Ghazal M (2017) Diabetic foot ulcer mobile detection system using smartphone thermal camera: a feasibility study. Biomed Eng Online 16(1):117. https:\/\/doi.org\/10.1186\/s12938-017-0408-x","journal-title":"Biomed Eng Online"},{"key":"14169_CR11","doi-asserted-by":"publisher","unstructured":"Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. IEEE 12th international conference on computer vision. Pp 349-356. https:\/\/doi.org\/10.1109\/ICCV.2009.5459271","DOI":"10.1109\/ICCV.2009.5459271"},{"key":"14169_CR12","doi-asserted-by":"publisher","unstructured":"Goodfellow I et al (2014) Generative adversarial networks. In Advances in Neural Information Processing Systems (NIPS). pp 2672\u20132680. https:\/\/doi.org\/10.1145\/3422622","DOI":"10.1145\/3422622"},{"key":"14169_CR13","doi-asserted-by":"publisher","first-page":"21815","DOI":"10.1007\/s11042-020-08980-w","volume":"79","author":"Y Gu","year":"2020","unstructured":"Gu Y, Zeng Z, Chen H, Wei J, Zhang Y, Chen B, Li Y, Qin Y, Xie Q, Jiang Z, Lu Y (2020) MedSRGAN: medical images super-resolution using generative adversarial networks. Multimed Tools Appl 79:21815\u201321840. https:\/\/doi.org\/10.1007\/s11042-020-08980-w","journal-title":"Multimed Tools Appl"},{"issue":"18","key":"14169_CR14","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1364\/AO.57.000D98","volume":"57","author":"A Guei","year":"2018","unstructured":"Guei A, Akhloufi M (2018) Deep learning enhancement of infrared face images using generative adversarial networks. Appl Opt 57(18):98\u2013D107. https:\/\/doi.org\/10.1364\/AO.57.000D98","journal-title":"Appl Opt"},{"key":"14169_CR15","volume-title":"Video colour variation detection and motion magnification to observe subtle changes","author":"H Javaid","year":"2013","unstructured":"Javaid H, Babar TK, Rasool A, Saghir RU (2013) Video colour variation detection and motion magnification to observe subtle changes. M.Sc. Thesis, Blekinge Institute of Technology, Faisalabad, Pakistan"},{"key":"14169_CR16","doi-asserted-by":"publisher","unstructured":"Johnson J, Alahi A, Li F (2016) Perceptual losses for real-time style transfer and super resolution. In European Conference on Computer Vision (ECCV) Springer pp 694\u2013711. https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"14169_CR17","doi-asserted-by":"publisher","unstructured":"Kim J, Lee JK, Lee KM (2016) accurate image super-resolution using very deep convolutional networks. IEEE CVPR pp 1646\u20131654. https:\/\/doi.org\/10.1109\/CVPR.2016.182","DOI":"10.1109\/CVPR.2016.182"},{"key":"14169_CR18","doi-asserted-by":"publisher","unstructured":"Ledig C et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. 2017 IEEE conference on computer vision and pattern recognition (CVPR) Honolulu. Pp 105-114. https:\/\/doi.org\/10.1109\/CVPR.2017.19","DOI":"10.1109\/CVPR.2017.19"},{"key":"14169_CR19","doi-asserted-by":"publisher","unstructured":"Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolutaion. IEEE conference on computer vision and pattern recognition workshops (CVPRW). Pp 1132\u20131140. https:\/\/doi.org\/10.1109\/CVPRW.2017.151","DOI":"10.1109\/CVPRW.2017.151"},{"key":"14169_CR20","doi-asserted-by":"publisher","unstructured":"Liu S et al (2019) Infrared image super resolution using Gan with infrared image prior. In: IEEE 4th International Conference on Signal and Image Processing (ICSIP), pp 1004\u20131009. https:\/\/doi.org\/10.1109\/SIPROCESS.2019.8868566","DOI":"10.1109\/SIPROCESS.2019.8868566"},{"issue":"3","key":"14169_CR21","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s12518-019-00253-y","volume":"11","author":"E Mandanici","year":"2019","unstructured":"Mandanici E, Tavasci L, Corsini F, Gandolfi S (2019) A multi-image super-resolution algorithm applied to thermal imagery. Appl Geomatics 11(3):215\u2013228. https:\/\/doi.org\/10.1007\/s12518-019-00253-y","journal-title":"Appl Geomatics"},{"issue":"10","key":"14169_CR22","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.1016\/j.cviu.2013.06.010","volume":"117","author":"K Nguyen","year":"2013","unstructured":"Nguyen K, Fookes C, Sridharan S, Sv D (2013) Feature-domain super-resolution for iris recognition. Comput Vis Image Underst 117(10):1526\u20131535. https:\/\/doi.org\/10.1016\/j.cviu.2013.06.010","journal-title":"Comput Vis Image Underst"},{"key":"14169_CR23","doi-asserted-by":"publisher","unstructured":"Ornek AH, Ceylan M, Ervural S (2019) Health status detection of neonates using infrared thermography and deep convolutional neural networks.\u00a0Infrared Phys Technol\u00a0103:103044. https:\/\/doi.org\/10.1016\/j.infrared.2019.103044","DOI":"10.1016\/j.infrared.2019.103044"},{"key":"14169_CR24","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434"},{"key":"14169_CR25","doi-asserted-by":"crossref","unstructured":"Rivadeneira RE, Suarez P, Sappa AD, Vintimilla BX (2019) Thermal image super resolution through deep convolutional neural network. International conference on image analysis and recognition. Pp 417\u2013426","DOI":"10.1007\/978-3-030-27272-2_37"},{"key":"14169_CR26","doi-asserted-by":"publisher","unstructured":"Senalp FM, Ceylan M (2020) Enhancement of low resolution thermal face image resolution using deep learning. European J Sci Tech. Pp 131-135. https:\/\/doi.org\/10.31590\/ejosat.802174","DOI":"10.31590\/ejosat.802174"},{"issue":"5","key":"14169_CR27","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.18280\/ts.380511","volume":"38","author":"FM Senalp","year":"2021","unstructured":"Senalp FM, Ceylan M (2021) Deep learning based super resolution and classification applications for neonatal thermal images. Traitement du signal 38(5):1361\u20131368. https:\/\/doi.org\/10.18280\/ts.380511","journal-title":"Traitement du signal"},{"key":"14169_CR28","doi-asserted-by":"publisher","first-page":"9313","DOI":"10.1007\/s11042-021-11436-4","volume":"81","author":"FM Senalp","year":"2022","unstructured":"Senalp FM, Ceylan M (2022) Effects of the deep learning-based super-resolution method on thermal image classification applications. Multimed Tools Appl 81:9313\u20139330. https:\/\/doi.org\/10.1007\/s11042-021-11436-4","journal-title":"Multimed Tools Appl"},{"key":"14169_CR29","first-page":"1556","volume":"1409","author":"K Simonyan","year":"2014","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Comput Vis Pattern Recognit arXiv 1409:1556","journal-title":"Comput Vis Pattern Recognit arXiv"},{"issue":"4","key":"14169_CR30","doi-asserted-by":"publisher","first-page":"043015","DOI":"10.1117\/1.JEI.24.4.043015","volume":"24","author":"K Singh","year":"2015","unstructured":"Singh K, Gupta A, Kapoor R (2015) Fingerprint image super-resolution via ridge orientation-based clustered coupled sparse dictionaries. J Electron Imaging 24(4):043015. https:\/\/doi.org\/10.1117\/1.JEI.24.4.043015","journal-title":"J Electron Imaging"},{"key":"14169_CR31","doi-asserted-by":"crossref","unstructured":"Sun L, Sakaridis C, Liang J, Jiang Q, Yang K, Sun P, Ye Y, Wang K, Van Gool L (2022) MEFNet: multi-scale event fusion network for motion deblurring with cross-modal attention. Computer Vision \u2013 ECCV 2022, pp 412\u2013428","DOI":"10.1007\/978-3-031-19797-0_24"},{"key":"14169_CR32","doi-asserted-by":"crossref","unstructured":"Toyran M (2008) Reconstructing super resolution images from low resolution images. M.Sc. Thesis, institute of science, Istanbul","DOI":"10.1109\/SIU.2008.4632537"},{"key":"14169_CR33","doi-asserted-by":"publisher","unstructured":"Voronin V, Semenishchev E, Frants V, Agaian S (2018) Smart cloud system for forensic thermal image enhancement using local and global logarithmic transform histogram matching. 2018 IEEE international conference on smart cloud (SmartCloud) New York. Pp 153-157. https:\/\/doi.org\/10.1109\/SmartCloud.2018.00033","DOI":"10.1109\/SmartCloud.2018.00033"},{"key":"14169_CR34","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/s00138-020-01073-6","volume":"31","author":"M Wang","year":"2020","unstructured":"Wang M, Chen Z, Wu QMJ, Jian M (2020) Improved face super-resolution generative adversarial networks. Mach Vis Appl 31:22. https:\/\/doi.org\/10.1007\/s00138-020-01073-6","journal-title":"Mach Vis Appl"},{"issue":"6","key":"14169_CR35","doi-asserted-by":"publisher","first-page":"3566","DOI":"10.1109\/TIM.2019.2932175","volume":"69","author":"D Weixiang","year":"2019","unstructured":"Weixiang D, Addepalli P, Zhao Y (2019) The spatial resolution enhancement for a Thermogram enabled by controlled sub-pixel movements. IEEE Trans Instrum Meas 69(6):3566\u20133575. https:\/\/doi.org\/10.1109\/TIM.2019.2932175","journal-title":"IEEE Trans Instrum Meas"},{"key":"14169_CR36","doi-asserted-by":"publisher","unstructured":"Yan R, Yang K, Wang K (2021) NLFNet: non-local fusion towards generalized multimodal semantic segmentation across RGB-depth, polarization, and thermal images. 2021 IEEE international conference on robotics and biomimetics (ROBIO), pp 1129-1135. https:\/\/doi.org\/10.1109\/ROBIO54168.2021.9739390","DOI":"10.1109\/ROBIO54168.2021.9739390"},{"key":"14169_CR37","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.sigpro.2016.05.002","volume":"128","author":"L Yue","year":"2018","unstructured":"Yue L, Shen H, Li J, Yuan Q, Zhang H, Zhang L (2018) Image super-resolution:the techniques, applications, and future. Signal Process 128:389\u2013408. https:\/\/doi.org\/10.1016\/j.sigpro.2016.05.002","journal-title":"Signal Process"},{"key":"14169_CR38","doi-asserted-by":"publisher","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. Proceedings of the European conference on computer vision. Pp 286\u2013301. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18","DOI":"10.1007\/978-3-030-01234-2_18"},{"issue":"8","key":"14169_CR39","doi-asserted-by":"publisher","first-page":"2587","DOI":"10.3390\/s18082587","volume":"18","author":"X Zhang","year":"2018","unstructured":"Zhang X, Li C, Meng Q, Liu S, Zhang Y, Wang J (2018) Infrared image super resolution by combining compressive sensing and deep learning. Sensors (Basel) 18(8):2587. https:\/\/doi.org\/10.3390\/s18082587","journal-title":"Sensors (Basel)"},{"key":"14169_CR40","doi-asserted-by":"publisher","unstructured":"Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. Comput Vis (ICCV) IEEE Int Conf, pp 2242\u20132251.https:\/\/doi.org\/10.1109\/ICCV.2017.244","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14169-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-14169-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14169-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T15:01:27Z","timestamp":1682002887000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-14169-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,17]]},"references-count":40,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["14169"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-14169-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2022,11,17]]},"assertion":[{"value":"19 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}