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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.12701 |
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| _version_ | 1866915496124743680 |
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| author | Jin, Wenzhuo Yang, Qianfeng Wu, Xianhao Chen, Hongming Li, Pengpeng Chen, Xiang |
| author_facet | Jin, Wenzhuo Yang, Qianfeng Wu, Xianhao Chen, Hongming Li, Pengpeng Chen, Xiang |
| contents | Early-stage fire scenes (0-15 minutes after ignition) represent a crucial temporal window for emergency interventions. During this stage, the smoke produced by combustion significantly reduces the visibility of surveillance systems, severely impairing situational awareness and hindering effective emergency response and rescue operations. Consequently, there is an urgent need to remove smoke from images to obtain clear scene information. However, the development of smoke removal algorithms remains limited due to the lack of large-scale, real-world datasets comprising paired smoke-free and smoke-degraded images. To address these limitations, we present a real-world surveillance image desmoking benchmark dataset named SmokeBench, which contains image pairs captured under diverse scenes setup and smoke concentration. The curated dataset provides precisely aligned degraded and clean images, enabling supervised learning and rigorous evaluation. We conduct comprehensive experiments by benchmarking a variety of desmoking methods on our dataset. Our dataset provides a valuable foundation for advancing robust and practical image desmoking in real-world fire scenes. This dataset has been released to the public and can be downloaded from https://github.com/ncfjd/SmokeBench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SmokeBench: A Real-World Dataset for Surveillance Image Desmoking in Early-Stage Fire Scenes Jin, Wenzhuo Yang, Qianfeng Wu, Xianhao Chen, Hongming Li, Pengpeng Chen, Xiang Computer Vision and Pattern Recognition Early-stage fire scenes (0-15 minutes after ignition) represent a crucial temporal window for emergency interventions. During this stage, the smoke produced by combustion significantly reduces the visibility of surveillance systems, severely impairing situational awareness and hindering effective emergency response and rescue operations. Consequently, there is an urgent need to remove smoke from images to obtain clear scene information. However, the development of smoke removal algorithms remains limited due to the lack of large-scale, real-world datasets comprising paired smoke-free and smoke-degraded images. To address these limitations, we present a real-world surveillance image desmoking benchmark dataset named SmokeBench, which contains image pairs captured under diverse scenes setup and smoke concentration. The curated dataset provides precisely aligned degraded and clean images, enabling supervised learning and rigorous evaluation. We conduct comprehensive experiments by benchmarking a variety of desmoking methods on our dataset. Our dataset provides a valuable foundation for advancing robust and practical image desmoking in real-world fire scenes. This dataset has been released to the public and can be downloaded from https://github.com/ncfjd/SmokeBench. |
| title | SmokeBench: A Real-World Dataset for Surveillance Image Desmoking in Early-Stage Fire Scenes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.12701 |