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.

SpikeMM 是一种基于 spike 的算法，专门为高速运动放大而设计，通过多级信息提取、空间上采样和运动放大模块的集成，实现了自我监督方法适用于各种场景，并与高性能超分辨率和运动放大算法无缝结合，经验证在实际高频设置中放大运动的能力。