  Conventional stereoscopic displays suffer from vergence-accommodation
conflict and cause visual fatigue. Integral-imaging-based displays resolve the
problem by directly projecting the sub-aperture views of a light field into the
eyes using a microlens array or a similar structure. However, such displays
have an inherent trade-off between angular and spatial resolutions. In this
paper, we propose a novel coded time-division multiplexing technique that
projects encoded sub-aperture views to the eyes of a viewer with correct cues
for vergence-accommodation reflex. Given sparse light field sub-aperture views,
our pipeline can provide a perception of high-resolution refocused images with
minimal aliasing by jointly optimizing the sub-aperture views for display and
the coded aperture pattern. This is achieved via deep learning in an end-to-end
fashion by simulating light transport and image formation with Fourier optics.
To our knowledge, this work is among the first that optimize the light field
display pipeline with deep learning. We verify our idea with objective image
quality metrics (PSNR, SSIM, and LPIPS) and perform an extensive study on
various customizable design variables in our display pipeline. Experimental
results show that light fields displayed using the proposed technique indeed
have higher quality than that of baseline display designs.
