Super-resolution image display using diffractive decoders
This work addresses the need for compact, low-power, and computationally efficient high-resolution displays, with potential applications in imaging and projection systems, though it is incremental as it builds on existing diffractive and deep learning methods.
The paper tackles the problem of high-resolution image synthesis and projection over a large field-of-view, which is limited by the space-bandwidth-product of wavefront modulators, by introducing a deep learning-enabled diffractive display design that achieves a super-resolution factor of ~4 and a ~16-fold increase in SBP.
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.