  Detecting and magnifying imperceptible high-frequency motions in real-world
scenarios has substantial implications for industrial and medical applications.
These motions are characterized by small amplitudes and high frequencies.
Traditional motion magnification methods rely on costly high-speed cameras or
active light sources, which limit the scope of their applications. In this
work, we propose a dual-camera system consisting of an event camera and a
conventional RGB camera for video motion magnification, providing
temporally-dense information from the event stream and spatially-dense data
from the RGB images. This innovative combination enables a broad and
cost-effective amplification of high-frequency motions. By revisiting the
physical camera model, we observe that estimating motion direction and
magnitude necessitates the integration of event streams with additional image
features. On this basis, we propose a novel deep network tailored for
event-based motion magnification. Our approach utilizes the Second-order
Recurrent Propagation module to proficiently interpolate multiple frames while
addressing artifacts and distortions induced by magnified motions.
Additionally, we employ a temporal filter to distinguish between noise and
useful signals, thus minimizing the impact of noise. We also introduced the
first event-based motion magnification dataset, which includes a synthetic
subset and a real-captured subset for training and benchmarking. Through
extensive experiments in magnifying small-amplitude, high-frequency motions, we
demonstrate the effectiveness and accuracy of our dual-camera system and
network, offering a cost-effective and flexible solution for motion detection
and magnification.
