TL;DR
This paper introduces a novel high-speed full-color video CGH generation method combining Spectrum-Guided Depth Division Multiplexing and a lightweight neural network architecture, achieving real-time 1080p holographic video with high fidelity.
Contribution
It proposes SGDDM for high-fidelity phase optimization and HoloMamba, a neural network that models spatial-temporal correlations, enabling fast and high-quality full-color holographic video.
Findings
HoloMamba achieves over 260 FPS for 1080p holographic video.
SGDDM maintains high color fidelity at high frame rates.
The combined approach outperforms previous methods in speed and quality.
Abstract
Computer-generated holography (CGH) is a promising technology for next-generation displays. However, generating high-speed, high-quality holographic video requires both high frame rate display and efficient computation, but is constrained by two key limitations: () Learning-based models often produce over-smoothed phases with narrow angular spectra, causing severe color crosstalk in high frame rate full-color displays such as depth-division multiplexing and thus resulting in a trade-off between frame rate and color fidelity. () Existing frame-by-frame optimization methods typically optimize frames independently, neglecting spatial-temporal correlations between consecutive frames and leading to computationally inefficient solutions. To overcome these challenges, in this paper, we propose a novel high-speed full-color video CGH generation scheme. First, we introduce Spectrum-Guided…
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