{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T02:23:11Z","timestamp":1777429391601,"version":"3.51.4"},"reference-count":69,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,9]],"date-time":"2025-02-09T00:00:00Z","timestamp":1739059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Higher Education and Science Committee"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Object tracking in thermal video is challenging due to noise, blur, and low contrast. We present TVEMamba, a Mamba-based enhancement framework with near-linear complexity that improves tracking in these conditions. Our approach uses a State Space 2D (SS2D) module integrated with Convolutional Neural Networks (CNNs) to filter, sharpen, and highlight important details. Key components include (i) a denoising module to reduce background noise and enhance image clarity, (ii) an optical flow attention module to handle complex motion and reduce blur, and (iii) entropy-based labeling to create a fully labeled thermal dataset for training and evaluation. TVEMamba outperforms existing methods (DCRGC, RLBHE, IE-CGAN, BBCNN) across multiple datasets (BIRDSAI, FLIR, CAMEL, Autonomous Vehicles, Solar Panels) and achieves higher scores on standard quality metrics (EME, BDIM, DMTE, MDIMTE, LGTA). Extensive tests, including ablation studies and convergence analysis, confirm its robustness. Real-world examples, such as tracking humans, animals, and moving objects for self-driving vehicles and remote sensing, demonstrate the practical value of TVEMamba.<\/jats:p>","DOI":"10.3390\/info16020125","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T03:39:47Z","timestamp":1739158787000},"page":"125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Thermal Video Enhancement Mamba: A Novel Approach to Thermal Video Enhancement for Real-World Applications"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1606-0759","authenticated-orcid":false,"given":"Sargis","family":"Hovhannisyan","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Mechanics, Yerevan State University, Yerevan 0025, Armenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4601-4507","authenticated-orcid":false,"given":"Sos","family":"Agaian","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Engineering, City University of New York (CUNY), New York, NY 10031, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9435-4536","authenticated-orcid":false,"given":"Karen","family":"Panetta","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, School of Engineering, Tufts University, Medford, MA 02155, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6683-0064","authenticated-orcid":false,"given":"Artyom","family":"Grigoryan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"170457","DOI":"10.1109\/ACCESS.2019.2955387","article-title":"A systematic review of intelligence video surveillance: Trends, techniques, frameworks, and datasets","volume":"7","author":"Shidik","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Karpuzov, S., Petkov, G., Ilieva, S., Petkov, A., and Kalitzin, S. 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