  Most computer vision systems and computational photography systems are
visible light based which is a small fraction of the electromagnetic (EM)
spectrum. In recent years radio frequency (RF) hardware has become more widely
available, for example, many cars are equipped with a RADAR, and almost every
home has a WiFi device. In the context of imaging, RF spectrum holds many
advantages compared to visible light systems. In particular, in this regime, EM
energy effectively interacts in different ways with matter. This property
allows for many novel applications such as privacy preserving computer vision
and imaging through absorbing and scattering materials in visible light such as
walls. Here, we expand many of the concepts in computational photography in
visible light to RF cameras. The main limitation of imaging with RF is the
large wavelength that limits the imaging resolution when compared to visible
light. However, the output of RF cameras is usually processed by computer
vision and perception algorithms which would benefit from multi-modal sensing
of the environment, and from sensing in situations in which visible light
systems fail. To bridge the gap between computational photography and RF
imaging, we expand the concept of light-field to RF. This work paves the way to
novel computational sensing systems with RF.
