| Title: | Video enhancement for increased spatio-temporal resolution in thermal videos : demonstration on a pool fire |
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| Authors: | ID Veit, Martin (Corresponding author)
ID Lucherini, Andrea (Author)
ID Verstockt, Steven (Author)
ID Merci, Bart (Author) |
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| Files: |  URL - Source URL, visit https://www.sciencedirect.com/science/article/pii/S0379711226001268
 PDF - Presentation file, download (6,85 MB)
MD5: D8155B0B3550FEDCA0C5C9D6F9AADC3F
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  ZAG - Slovenian National Building and Civil Engineering Institute
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| Abstract: | A spatio-temporal video enhancement of a small-scale pool fire is performed to address the typically low spatial resolution and frame rate of inexpensive infrared (IR) cameras. Improving image quality can increase the applicability of low-cost thermal cameras for certain research tasks and analyses. The spatial resolution and frame rate are doubled, from 310 × 250 pixels (px) to 620 × 500 px, and from 25 frames per second (fps) to 50 fps, as well as from 50 fps to 100 fps. Spatial resolution enhancement is achieved using super-resolution methods based on deep learning, employing several pre-trained models: Fast Super-Resolution CNN (FSRCNN), Efficient Sub-Pixel Convolutional Network (ESPCN), Enhanced Deep Super-Resolution (EDSR), Laplacian Pyramid Super-Resolution Network (LapSRN), and Real-ESRGAN. The footage consists of an n-heptane pool fire recorded using a mid-wave infrared (MWIR) FLIR X6981 HS InSb camera. EDSR provides the best performance for both purely resized images and images subjected to complex degradation. For temporal enhancement, a pre-trained frame interpolation model, FLAVR (FlowAgnostic Video Representation), is used. The resulting interpolated frames appear realistic and preserve the overall flow direction and shape of the flame. The interpolated frames are compared with ground-truth data to validate the accuracy of the temporal enhancement. |
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| Keywords: | image processing, thermal camera, machine learning, pool fire |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 03.04.2026 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2026 |
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| Number of pages: | str. 1-11 |
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| Numbering: | Vol. 163, [article no.] 104758 |
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| PID: | 20.500.12556/DiRROS-30095  |
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| UDC: | 614.84 |
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| ISSN on article: | 1873-7226 |
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| DOI: | 10.1016/j.firesaf.2026.104758 |
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| COBISS.SI-ID: | 280256003 |
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| Copyright: | © 2026 The Authors |
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| Note: |
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| Publication date in DiRROS: | 15.06.2026 |
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| Views: | 79 |
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| Downloads: | 54 |
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