  Holography is a vital tool used in various applications from microscopy,
solar energy, imaging, display to information encryption. Generation of a
holographic image and reconstruction of object/hologram information from a
holographic image using the current algorithms are time-consuming processes.
Versatile, fast in the meantime accurate methodologies are required to compute
holograms performing color imaging at multiple observation planes and
reconstruct object/sample information from a holographic image for widely
accommodating optical holograms. Here, we focus on design of optical holograms
for generation of holographic images at multiple observation planes and colors
via a deep learning model the DOENet. The DOENet produces optical holograms
which show multitasking performance as multiplexing color holographic image
planes by tuning holographic structures. Furthermore, our deep learning model
retrieves an object/hologram information from an intensity holographic image
without requiring phase and amplitude information from the intensity image. We
show that reconstructed objects/holograms show excellent agreement with the
ground-truths. The DOENet does not need iteratively reconstruction of
object/hologram information while conventional object/hologram recovery methods
rely on multiple holographic images at various observation planes along with
the iterative algorithms. We believe that the DOENet based object/hologram
reconstruction and generation of holographic images may speed up wide-area
implementation of optical holography.
