High-Speed FHD Full-Color Video Computer-Generated Holography
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Abstract (arXiv)
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: (i) 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. (ii) 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 Depth Division Multiplexing (SGDDM), which optimizes phase distributions via frequency modulation, enabling high-fidelity full-color display at high frame rates. Second, we present HoloMamba, a lightweight asymmetric Mamba-Unet architecture that explicitly models spatial-temporal correlations across video sequences to enhance reconstruction quality and computational efficiency. Extensive simulated and real-world experiments demonstrate that SGDDM achieves high-fidelity full-color display without compromise in frame rate, while HoloMamba generates FHD (1080p) full-color holographic video at over 260 FPS, more than 2.6× faster than the prior state-of-the-art Divide-Conquer-and-Merge Strategy.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 36 references that are in the map. First posted on arXiv 27 Aug 2025.
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10 of 36 references.
References · 10
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2021Is Space-Time Attention All You Need for Video Understanding?cited by 1,620
- 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
- 2017The 2017 DAVIS Challenge on Video Object Segmentationcited by 957
- 2024VideoMamba: State Space Model for Efficient Video Understandingcited by 328
- 2014Phase Retrieval via Wirtinger Flow: Theory and Algorithmscited by 125
- 2019Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGANcited by 87
- 2018Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrievalcited by 71
- 2024Exploiting Multimodal Spatial-temporal Patterns for Video Object Trackingcited by 57
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- 2024Configurable Holography: Towards Display and Scene Adaptation
- 2022Time-multiplexed Neural Holography: A flexible framework for holographic near-eye displays with fast heavily-quantized spatial light modulators
- 2022High-contrast, speckle-free, true 3D holography via binary CGH optimization
- 2024P-Hologen: An End-to-End Generative Framework for Phase-Only Holograms
- 2025RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression
- 2024Quantized neural network for complex hologram generation
- 2025HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling
- 2025HiStream: Efficient High-Resolution Video Generation via Redundancy-Eliminated Streaming