FTPFusion: Frequency-Aware Infrared and Visible Video Fusion with Temporal Perturbation
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915909302484992 |
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| author | Li, Xilai Fang, Chusheng Li, Xiaosong |
| author_facet | Li, Xilai Fang, Chusheng Li, Xiaosong |
| contents | Infrared and visible video fusion plays a critical role in intelligent surveillance and low-light monitoring. However, maintaining temporal stability while preserving spatial detail remains a fundamental challenge. Existing methods either focus on frame-wise enhancement with limited temporal modeling or rely on heavy spatio-temporal aggregation that often sacrifices high-frequency details. In this paper, we propose FTPFusion, a frequency-aware infrared and visible video fusion method based on temporal perturbation and sparse cross-modal interaction. Specifically, FTPFusion decomposes the feature representations into high-frequency and low-frequency components for collaborative modeling. The high-frequency branch performs sparse cross-modal spatio-temporal interaction to capture motion-related context and complementary details. The low-frequency branch introduces a temporal perturbation strategy to enhance robustness against complex video variations, such as flickering, jitter, and local misalignment. Furthermore, we design an offset-aware temporal consistency constraint to explicitly stabilize cross-frame representations under temporal disturbances. Extensive experiments on multiple public benchmarks demonstrate that FTPFusion consistently outperforms state-of-the-art methods across multiple metrics in both spatial fidelity and temporal consistency. The source code will be available at https://github.com/ixilai/FTPFusion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01900 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FTPFusion: Frequency-Aware Infrared and Visible Video Fusion with Temporal Perturbation Li, Xilai Fang, Chusheng Li, Xiaosong Computer Vision and Pattern Recognition Infrared and visible video fusion plays a critical role in intelligent surveillance and low-light monitoring. However, maintaining temporal stability while preserving spatial detail remains a fundamental challenge. Existing methods either focus on frame-wise enhancement with limited temporal modeling or rely on heavy spatio-temporal aggregation that often sacrifices high-frequency details. In this paper, we propose FTPFusion, a frequency-aware infrared and visible video fusion method based on temporal perturbation and sparse cross-modal interaction. Specifically, FTPFusion decomposes the feature representations into high-frequency and low-frequency components for collaborative modeling. The high-frequency branch performs sparse cross-modal spatio-temporal interaction to capture motion-related context and complementary details. The low-frequency branch introduces a temporal perturbation strategy to enhance robustness against complex video variations, such as flickering, jitter, and local misalignment. Furthermore, we design an offset-aware temporal consistency constraint to explicitly stabilize cross-frame representations under temporal disturbances. Extensive experiments on multiple public benchmarks demonstrate that FTPFusion consistently outperforms state-of-the-art methods across multiple metrics in both spatial fidelity and temporal consistency. The source code will be available at https://github.com/ixilai/FTPFusion. |
| title | FTPFusion: Frequency-Aware Infrared and Visible Video Fusion with Temporal Perturbation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.01900 |