  Computational imaging has been playing a vital role in the development of
natural sciences. Advances in sensory, information, and computer technologies
have further extended the scope of influence of imaging, making digital images
an essential component of our daily lives. For the past three decades, we have
witnessed phenomenal developments of mathematical and machine learning methods
in computational imaging. In this note, we will review some of the recent
developments of the machine learning approach for computational imaging and
discuss its differences and relations to the mathematical approach. We will
demonstrate how we may combine the wisdom from both approaches, discuss the
merits and potentials of such a combination and present some of the new
computational and theoretical challenges it brings about.
