Scholarlink

<p>Real-time, high-quality computer-generated holography (CGH) is essential for next-generation virtual reality or augmented reality displays. Existing deep learning methods often struggle to balance computational efficiency with reconstruction fidelity. Complex-valued convolutional neural networks (CCNNs) are well-suited for phase-only hologram generation. However, achieving real-time, high-fidelity holograms remains challenging due to three key issues: insufficient modeling of complex-field feature interactions, phase distortion during up-sampling, and a lack of integration with physical optics principles. This study proposes an end-to-end physics-aware complex-valued attention network (PACAN-CGH) to address these challenges. Its core innovations include a complex-valued hybrid attention module for adaptive feature selection, a phase-continuous up-sampling layer based on complex-valued sub-pixel convolution, and a physics-driven loss function incorporating band-limited diffraction constraints. This co-design ensures high-quality hologram generation that is both computational efficiency and physical consistency. Experiments validate the superiority of PACAN-CGH, achieving an average Peak Signal-to-Noise Ratio of 33.31 dB at 1920<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation>1072 resolution with fast inference time of 0.36 seconds per frame. Ablation studies confirm the contribution of each component, and cross-dataset tests demonstrate superior generalization capability. This work bridges optical physics with neural network design, establishing a new paradigm for efficient and physically interpretable CGH, and advancing complex-valued neural network design.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

PACAN-CGH: a physics-aware complex-valued attention network for real-time and high-quality computer-generated hologram

  • 期刊论文

  • 发表日期：

    2026年3月3日

查看全文
  • Xiaofei Nie,
  • Yudi Zhao,
  • Qiang He,
  • Kai Zhao

摘要

Real-time, high-quality computer-generated holography (CGH) is essential for next-generation virtual reality or augmented reality displays. Existing deep learning methods often struggle to balance computational efficiency with reconstruction fidelity. Complex-valued convolutional neural networks (CCNNs) are well-suited for phase-only hologram generation. However, achieving real-time, high-fidelity holograms remains challenging due to three key issues: insufficient modeling of complex-field feature interactions, phase distortion during up-sampling, and a lack of integration with physical optics principles. This study proposes an end-to-end physics-aware complex-valued attention network (PACAN-CGH) to address these challenges. Its core innovations include a complex-valued hybrid attention module for adaptive feature selection, a phase-continuous up-sampling layer based on complex-valued sub-pixel convolution, and a physics-driven loss function incorporating band-limited diffraction constraints. This co-design ensures high-quality hologram generation that is both computational efficiency and physical consistency. Experiments validate the superiority of PACAN-CGH, achieving an average Peak Signal-to-Noise Ratio of 33.31 dB at 1920 \(\times\) 1072 resolution with fast inference time of 0.36 seconds per frame. Ablation studies confirm the contribution of each component, and cross-dataset tests demonstrate superior generalization capability. This work bridges optical physics with neural network design, establishing a new paradigm for efficient and physically interpretable CGH, and advancing complex-valued neural network design.