SpikeMM: Flexi-Magnification of High-Speed Micro-Motions
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929369381863424 |
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| author | Zhang, Baoyue Zheng, Yajing Chen, Shiyan Zhang, Jiyuan Chen, Kang Yu, Zhaofei Huang, Tiejun |
| author_facet | Zhang, Baoyue Zheng, Yajing Chen, Shiyan Zhang, Jiyuan Chen, Kang Yu, Zhaofei Huang, Tiejun |
| contents | The amplification of high-speed micro-motions holds significant promise, with applications spanning fault detection in fast-paced industrial environments to refining precision in medical procedures. However, conventional motion magnification algorithms often encounter challenges in high-speed scenarios due to low sampling rates or motion blur. In recent years, spike cameras have emerged as a superior alternative for visual tasks in such environments, owing to their unique capability to capture temporal and spatial frequency domains with exceptional fidelity. Unlike conventional cameras, which operate at fixed, low frequencies, spike cameras emulate the functionality of the retina, asynchronously capturing photon changes at each pixel position using spike streams. This innovative approach comprehensively records temporal and spatial visual information, rendering it particularly suitable for magnifying high-speed micro-motions.This paper introduces SpikeMM, a pioneering spike-based algorithm tailored specifically for high-speed motion magnification. SpikeMM integrates multi-level information extraction, spatial upsampling, and motion magnification modules, offering a self-supervised approach adaptable to a wide range of scenarios. Notably, SpikeMM facilitates seamless integration with high-performance super-resolution and motion magnification algorithms. We substantiate the efficacy of SpikeMM through rigorous validation using scenes captured by spike cameras, showcasing its capacity to magnify motions in real-world high-frequency settings. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_00383 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SpikeMM: Flexi-Magnification of High-Speed Micro-Motions Zhang, Baoyue Zheng, Yajing Chen, Shiyan Zhang, Jiyuan Chen, Kang Yu, Zhaofei Huang, Tiejun Computer Vision and Pattern Recognition The amplification of high-speed micro-motions holds significant promise, with applications spanning fault detection in fast-paced industrial environments to refining precision in medical procedures. However, conventional motion magnification algorithms often encounter challenges in high-speed scenarios due to low sampling rates or motion blur. In recent years, spike cameras have emerged as a superior alternative for visual tasks in such environments, owing to their unique capability to capture temporal and spatial frequency domains with exceptional fidelity. Unlike conventional cameras, which operate at fixed, low frequencies, spike cameras emulate the functionality of the retina, asynchronously capturing photon changes at each pixel position using spike streams. This innovative approach comprehensively records temporal and spatial visual information, rendering it particularly suitable for magnifying high-speed micro-motions.This paper introduces SpikeMM, a pioneering spike-based algorithm tailored specifically for high-speed motion magnification. SpikeMM integrates multi-level information extraction, spatial upsampling, and motion magnification modules, offering a self-supervised approach adaptable to a wide range of scenarios. Notably, SpikeMM facilitates seamless integration with high-performance super-resolution and motion magnification algorithms. We substantiate the efficacy of SpikeMM through rigorous validation using scenes captured by spike cameras, showcasing its capacity to magnify motions in real-world high-frequency settings. |
| title | SpikeMM: Flexi-Magnification of High-Speed Micro-Motions |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.00383 |