  High-resolution synthesis/projection of images over a large field-of-view
(FOV) is hindered by the restricted space-bandwidth-product (SBP) of wavefront
modulators. We report a deep learning-enabled diffractive display design that
is based on a jointly-trained pair of an electronic encoder and a diffractive
optical decoder to synthesize/project super-resolved images using
low-resolution wavefront modulators. The digital encoder, composed of a trained
convolutional neural network (CNN), rapidly pre-processes the high-resolution
images of interest so that their spatial information is encoded into
low-resolution (LR) modulation patterns, projected via a low SBP wavefront
modulator. The diffractive decoder processes this LR encoded information using
thin transmissive layers that are structured using deep learning to
all-optically synthesize and project super-resolved images at its output FOV.
Our results indicate that this diffractive image display can achieve a
super-resolution factor of ~4, demonstrating a ~16-fold increase in SBP. We
also experimentally validate the success of this diffractive super-resolution
display using 3D-printed diffractive decoders that operate at the THz spectrum.
This diffractive image decoder can be scaled to operate at visible wavelengths
and inspire the design of large FOV and high-resolution displays that are
compact, low-power, and computationally efficient.
