  Video motion magnification techniques allow us to see small motions
previously invisible to the naked eyes, such as those of vibrating airplane
wings, or swaying buildings under the influence of the wind. Because the motion
is small, the magnification results are prone to noise or excessive blurring.
The state of the art relies on hand-designed filters to extract representations
that may not be optimal. In this paper, we seek to learn the filters directly
from examples using deep convolutional neural networks. To make training
tractable, we carefully design a synthetic dataset that captures small motion
well, and use two-frame input for training. We show that the learned filters
achieve high-quality results on real videos, with less ringing artifacts and
better noise characteristics than previous methods. While our model is not
trained with temporal filters, we found that the temporal filters can be used
with our extracted representations up to a moderate magnification, enabling a
frequency-based motion selection. Finally, we analyze the learned filters and
show that they behave similarly to the derivative filters used in previous
works. Our code, trained model, and datasets will be available online.
