TY - JOUR
AU - Matusik, Wojciech
AB - Computer-generated holography (CGH) provides volumetric control of coherent wavefront and is fundamental to applications such as volumetric 3D displays, lithography, neural photostimulation, and optical/acoustic trapping. Recently, deep learning-based methods emerged as promising computational paradigms for CGH synthesis that overcome the quality-runtime tradeoff in conventional simulation/optimization-based methods. Yet, the quality of the predicted hologram is intrinsically bounded by the dataset’s quality. Here we introduce a new hologram dataset, MIT-CGH-4K-V2, that uses a layered depth image as a data-efficient volumetric 3D input and a two-stage supervised+unsupervised training protocol for direct synthesis of high-quality 3D phase-only holograms. The proposed system also corrects vision aberration, allowing customization for end-users. We experimentally show photorealistic 3D holographic projections and discuss relevant spatial light modulator calibration procedures. Our method runs in real-time on a consumer GPU and 5 FPS on an iPhone 13 Pro, promising drastically enhanced performance for the applications above.
TI - End-to-end learning of 3D phase-only holograms for holographic display
JF - Light:Science & Applications
DO - 10.1038/s41377-022-00894-6
DA - 2022-08-03
UR - https://www.deepdyve.com/lp/springer-journals/end-to-end-learning-of-3d-phase-only-holograms-for-holographic-display-m4X1OF9HNF
VL - 11
IS - 1
DP - DeepDyve
ER -