{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T20:19:53Z","timestamp":1769631593297,"version":"3.49.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031114311","type":"print"},{"value":"9783031114328","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T00:00:00Z","timestamp":1658880000000},"content-version":"vor","delay-in-days":207,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Humans are able to perceive objects only in the visible spectrum range which limits the perception abilities in poor weather or low illumination conditions. The limitations are usually handled through technological advancements in thermographic imaging. However, thermal cameras have poor spatial resolutions compared to RGB cameras. Super-resolution (SR) techniques are commonly used to improve the overall quality of low-resolution images. There has been a major shift of research among the Computer Vision researchers towards SR techniques particularly aimed for thermal images. This paper analyzes the performance of three deep learning-based state-of-the-art SR algorithms namely Enhanced Deep Super Resolution (EDSR), Residual Channel Attention Network (RCAN) and Residual Dense Network (RDN) on thermal images. The algorithms were trained from scratch for different upscaling factors of \u00d72 and \u00d74. The dataset was generated from two different thermal imaging sequences of BU-TIV benchmark. The sequences contain both sparse and highly dense type of crowds with a far field camera view. The trained models were then used to super-resolve unseen test images. The quantitative analysis of the test images was performed using common image quality metrics such as PSNR, SSIM and LPIPS, while qualitative analysis was provided by evaluating effectiveness of the algorithms for crowd counting application. After only 54 and 51 epochs of RCAN and RDN respectively, both approaches were able to output average scores of 37.878, 0.986, 0.0098 and 30.175, 0.945, 0.0636 for PSNR, SSIM and LPIPS respectively. The EDSR algorithm took the least computation time during both training and testing because of its simple architecture. This research proves that a reasonable accuracy can be achieved with fewer training epochs when an application-specific dataset is carefully selected.<\/jats:p>","DOI":"10.1007\/978-3-031-11432-8_7","type":"book-chapter","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T13:18:01Z","timestamp":1658927881000},"page":"75-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images: Case Study of Crowd Counting"],"prefix":"10.1007","author":[{"given":"Syed Zeeshan","family":"Rizvi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Umar","family":"Farooq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rana Hammad","family":"Raza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"7_CR1","unstructured":"NASA. Visible Light | Science Mission Directorate. https:\/\/science.nasa.gov\/ems\/09_visiblelight. Accessed 15 Nov 2021"},{"issue":"1","key":"7_CR2","doi-asserted-by":"publisher","first-page":"62","DOI":"10.3390\/s16010062.10.1007\/s00521-021-05973-0","volume":"16","author":"M Kristoffersen","year":"2016","unstructured":"Kristoffersen, M., Dueholm, J., Gade, R., Moeslund, T.: Pedestrian counting with occlusion handling using stereo thermal cameras. Sensors 16(1), 62 (2016). https:\/\/doi.org\/10.3390\/s16010062.10.1007\/s00521-021-05973-0","journal-title":"Sensors"},{"issue":"5","key":"7_CR3","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/mce.2019.2923926","volume":"8","author":"SL Fernandes","year":"2019","unstructured":"Fernandes, S.L., Rajinikanth, V., Kadry, S.: A hybrid framework to evaluate breast abnormality using infrared thermal images. IEEE Consum. Electron. Mag 8(5), 31\u201336 (2019). https:\/\/doi.org\/10.1109\/mce.2019.2923926","journal-title":"IEEE Consum. Electron. Mag"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Ghose, D., et al.: Pedestrian detection in thermal images using saliency maps: In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshop (2019)","DOI":"10.1109\/CVPRW.2019.00130"},{"key":"7_CR5","doi-asserted-by":"publisher","unstructured":"Zeng, X., Miao, Y., Ubaid, S., Gao, X., Zhuang, S.: Detection and classification of bruises of pears based on thermal images. Postharv. Biol. Technol. 161, 111090 (2020). https:\/\/doi.org\/10.1016\/j.postharvbio.2019.111090","DOI":"10.1016\/j.postharvbio.2019.111090"},{"key":"7_CR6","doi-asserted-by":"publisher","unstructured":"Patel, H., et al.: ThermISRnet: an efficient thermal image super-resolution network. Opt. Eng. 60(07) (2020). https:\/\/doi.org\/10.1117\/1.oe.60.7.073101.10.1038\/s41598-020-77979-y","DOI":"10.1117\/1.oe.60.7.073101.10.1038\/s41598-020-77979-y"},{"key":"7_CR7","doi-asserted-by":"publisher","unstructured":"Ahmadi, S., et al.: Laser excited super resolution thermal imaging for nondestructive inspection of internal defects. Sci. Rep. 10(1) (2020). https:\/\/doi.org\/10.1038\/s41598-020-77979-y","DOI":"10.1038\/s41598-020-77979-y"},{"key":"7_CR8","doi-asserted-by":"publisher","unstructured":"Kuni Zoetgnande, Y.W., Dillenseger, J.-L., Alirezaie, J.: Edge focused super-resolution of thermal images. In: 2019 International Joint Conference on Neural Networks (IJCNN) (2019). https:\/\/doi.org\/10.1109\/ijcnn.2019.8852320.10.3390\/s21041265","DOI":"10.1109\/ijcnn.2019.8852320.10.3390\/s21041265"},{"key":"7_CR9","doi-asserted-by":"publisher","unstructured":"Raimundo, J., Lopez-Cuervo Medina, S., Prieto, J.F., Aguirre de Mata, J.: Super resolution infrared thermal imaging using Pansharpening algorithms: quantitative assessment and application to UAV thermal imaging. Sensors 21(4), 1265 (2020). https:\/\/doi.org\/10.3390\/s21041265","DOI":"10.3390\/s21041265"},{"key":"7_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/978-3-030-27272-2_37","volume-title":"Image Analysis and Recognition","author":"RE Rivadeneira","year":"2019","unstructured":"Rivadeneira, R.E., Su\u00e1rez, P.L., Sappa, A.D., Vintimilla, B.X.: Thermal image SuperResolution through deep convolutional neural network. In: Karray, F., Campilho, A., Yu, A. (eds.) ICIAR 2019. LNCS, vol. 11663, pp. 417\u2013426. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-27272-2_37"},{"key":"7_CR11","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https:\/\/doi.org\/10.1109\/cvpr.2016.90","DOI":"10.1109\/cvpr.2016.90"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Chudasama, V., et al.: Therisurnet-a computationally efficient thermal image super-resolution network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2020)","DOI":"10.1109\/CVPRW50498.2020.00051"},{"key":"7_CR13","unstructured":"Panagiotopoulou, A., Anastassopoulos, A.: Super-resolution reconstruction of thermal infrared images. In: Proceedings of the 4th WSEAS International Conference on REMOTE SENSING (2008)"},{"key":"7_CR14","doi-asserted-by":"publisher","unstructured":"Chen, X., Zhai, G., Wang, J., Hu, C., Chen, Y.: Color guided thermal image super resolution. In: 2016 Visual Communications and Image Processing (VCIP) (2016). https:\/\/doi.org\/10.1109\/vcip.2016.7805509","DOI":"10.1109\/vcip.2016.7805509"},{"key":"7_CR15","doi-asserted-by":"publisher","unstructured":"Jino Hans, W., Venkateswaran, N.: An efficient super-resolution algorithm for IR thermal images based on sparse representation. In: Proceedings of the 2015 Asia International Conference on Quantitative InfraRed Thermography (2015). https:\/\/doi.org\/10.21611\/qirt.2015.0092.10.3390\/rs12101642","DOI":"10.21611\/qirt.2015.0092.10.3390\/rs12101642"},{"issue":"10","key":"7_CR16","doi-asserted-by":"publisher","first-page":"1642","DOI":"10.3390\/rs12101642","volume":"12","author":"P Cascarano","year":"2020","unstructured":"Cascarano, P., et al.: Super-resolution of thermal images using an automatic total variation based method. Remote Sens. 12(10), 1642 (2020). https:\/\/doi.org\/10.3390\/rs12101642","journal-title":"Remote Sens."},{"key":"7_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-319-10593-2_13","volume-title":"Computer Vision \u2013 ECCV 2014","author":"C Dong","year":"2014","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184\u2013199. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10593-2_13"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Lim, B., et al.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Kansal, P., Nathan, S.: A multi-level supervision model: a novel approach for thermal image super resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshop. (2020)","DOI":"10.1109\/CVPRW50498.2020.00055"},{"key":"7_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-030-01234-2_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294\u2013310. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18"},{"key":"7_CR22","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Tian, Y., Kong, Y.; Zhong, B., Fu, Y: Residual dense network for image super-resolution. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2018). https:\/\/doi.org\/10.1109\/cvpr.2018.00262","DOI":"10.1109\/cvpr.2018.00262"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Liang, D., Xu, W., Zhu, Y., Zhou, Y.: Focal inverse distance transform maps for crowd localization and counting in dense crowd. arXiv:2102.07925 [cs] (2021)","DOI":"10.1109\/TMM.2022.3203870"},{"key":"7_CR24","doi-asserted-by":"publisher","unstructured":"Wu, Z., Fuller, N., Theriault, D., Betke, M.: A thermal infrared video benchmark for visual analysis. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (2014). https:\/\/doi.org\/10.1109\/cvprw.2014.39","DOI":"10.1109\/cvprw.2014.39"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Idrees, H., et al.: Composition loss for counting, density map estimation and localization in dense crowds. arXiv:1808.01050 [cs] (2018)","DOI":"10.1007\/978-3-030-01216-8_33"}],"container-title":["Lecture Notes in Networks and Systems","Digital Interaction and Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-11432-8_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,12]],"date-time":"2023-02-12T18:41:30Z","timestamp":1676227290000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-11432-8_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031114311","9783031114328"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-11432-8_7","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIDI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference on Multimedia, Interaction, Design and Innovation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"midi2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/midi2021.opi.org.pl\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}