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

Time-Division Multiplexing Light Field Display with Learned Coded Aperture