SWIR-LightFusion: Multi-spectral Semantic Fusion of Synthetic SWIR with Thermal IR (LWIR/MWIR) and RGB
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| Format: | Preprint |
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2025
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| _version_ | 1866915634333351936 |
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| author | Hussain, Muhammad Ishfaq Van Linh, Ma Naz, Zubia Fatima, Unse Ko, Yeongmin Jeon, Moongu |
| author_facet | Hussain, Muhammad Ishfaq Van Linh, Ma Naz, Zubia Fatima, Unse Ko, Yeongmin Jeon, Moongu |
| contents | Enhancing scene understanding in adverse visibility conditions remains a critical challenge for surveillance and autonomous navigation systems. Conventional imaging modalities, such as RGB and thermal infrared (MWIR / LWIR), when fused, often struggle to deliver comprehensive scene information, particularly under conditions of atmospheric interference or inadequate illumination. To address these limitations, Short-Wave Infrared (SWIR) imaging has emerged as a promising modality due to its ability to penetrate atmospheric disturbances and differentiate materials with improved clarity. However, the advancement and widespread implementation of SWIR-based systems face significant hurdles, primarily due to the scarcity of publicly accessible SWIR datasets. In response to this challenge, our research introduces an approach to synthetically generate SWIR-like structural/contrast cues (without claiming spectral reproduction) images from existing LWIR data using advanced contrast enhancement techniques. We then propose a multimodal fusion framework integrating synthetic SWIR, LWIR, and RGB modalities, employing an optimized encoder-decoder neural network architecture with modality-specific encoders and a softmax-gated fusion head. Comprehensive experiments on public RGB-LWIR benchmarks (M3FD, TNO, CAMEL, MSRS, RoadScene) and an additional private real RGB-MWIR-SWIR dataset demonstrate that our synthetic-SWIR-enhanced fusion framework improves fused-image quality (contrast, edge definition, structural fidelity) while maintaining real-time performance. We also add fair trimodal baselines (LP, LatLRR, GFF) and cascaded trimodal variants of U2Fusion/SwinFusion under a unified protocol. The outcomes highlight substantial potential for real-world applications in surveillance and autonomous systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13404 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SWIR-LightFusion: Multi-spectral Semantic Fusion of Synthetic SWIR with Thermal IR (LWIR/MWIR) and RGB Hussain, Muhammad Ishfaq Van Linh, Ma Naz, Zubia Fatima, Unse Ko, Yeongmin Jeon, Moongu Machine Learning Enhancing scene understanding in adverse visibility conditions remains a critical challenge for surveillance and autonomous navigation systems. Conventional imaging modalities, such as RGB and thermal infrared (MWIR / LWIR), when fused, often struggle to deliver comprehensive scene information, particularly under conditions of atmospheric interference or inadequate illumination. To address these limitations, Short-Wave Infrared (SWIR) imaging has emerged as a promising modality due to its ability to penetrate atmospheric disturbances and differentiate materials with improved clarity. However, the advancement and widespread implementation of SWIR-based systems face significant hurdles, primarily due to the scarcity of publicly accessible SWIR datasets. In response to this challenge, our research introduces an approach to synthetically generate SWIR-like structural/contrast cues (without claiming spectral reproduction) images from existing LWIR data using advanced contrast enhancement techniques. We then propose a multimodal fusion framework integrating synthetic SWIR, LWIR, and RGB modalities, employing an optimized encoder-decoder neural network architecture with modality-specific encoders and a softmax-gated fusion head. Comprehensive experiments on public RGB-LWIR benchmarks (M3FD, TNO, CAMEL, MSRS, RoadScene) and an additional private real RGB-MWIR-SWIR dataset demonstrate that our synthetic-SWIR-enhanced fusion framework improves fused-image quality (contrast, edge definition, structural fidelity) while maintaining real-time performance. We also add fair trimodal baselines (LP, LatLRR, GFF) and cascaded trimodal variants of U2Fusion/SwinFusion under a unified protocol. The outcomes highlight substantial potential for real-world applications in surveillance and autonomous systems. |
| title | SWIR-LightFusion: Multi-spectral Semantic Fusion of Synthetic SWIR with Thermal IR (LWIR/MWIR) and RGB |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.13404 |