  Many important physical phenomena involve subtle signals that are difficult
to observe with the unaided eye, yet visualizing them can be very informative.
Current motion magnification techniques can reveal these small temporal
variations in video, but require precise prior knowledge about the target
signal, and cannot deal with interference motions at a similar frequency. We
present DeepMag an end-to-end deep neural video-processing framework based on
gradient ascent that enables automated magnification of subtle color and motion
signals from a specific source, even in the presence of large motions of
various velocities. While the approach is generalizable, the advantages of
DeepMag are highlighted via the task of video-based physiological
visualization. Through systematic quantitative and qualitative evaluation of
the approach on videos with different levels of head motion, we compare the
magnification of pulse and respiration to existing state-of-the-art methods.
Our method produces magnified videos with substantially fewer artifacts and
blurring whilst magnifying the physiological changes by a similar degree.
