  Phase retrieval in optical imaging refers to the recovery of a complex signal
from phaseless data acquired in the form of its diffraction patterns. These
patterns are acquired through a system with a coherent light source that
employs a diffractive optical element (DOE) to modulate the scene resulting in
coded diffraction patterns at the sensor. Recently, the hybrid approach of
model-driven network or deep unfolding has emerged as an effective alternative
to conventional model-based and learning-based phase retrieval techniques
because it allows for bounding the complexity of algorithms while also
retaining their efficacy. Additionally, such hybrid approaches have shown
promise in improving the design of DOEs that follow theoretical uniqueness
conditions. There are opportunities to exploit novel experimental setups and
resolve even more complex DOE phase retrieval applications. This paper presents
an overview of algorithms and applications of deep unfolding for bootstrapped -
regardless of near, middle, and far zones - phase retrieval.
