  Point-spread-function (PSF) engineering is a powerful computational imaging
techniques wherein a custom phase mask is integrated into an optical system to
encode additional information into captured images. Used in combination with
deep learning, such systems now offer state-of-the-art performance at monocular
depth estimation, extended depth-of-field imaging, lensless imaging, and other
tasks. Inspired by recent advances in spatial light modulator (SLM) technology,
this paper answers a natural question: Can one encode additional information
and achieve superior performance by changing a phase mask dynamically over
time? We first prove that the set of PSFs described by static phase masks is
non-convex and that, as a result, time-averaged PSFs generated by dynamic phase
masks are fundamentally more expressive. We then demonstrate, in simulation,
that time-averaged dynamic (TiDy) phase masks can offer substantially improved
monocular depth estimation and extended depth-of-field imaging performance.
