P-Hologen: An End-to-End Generative Framework for Phase-Only Holograms

• URL: http://arxiv.org/abs/2404.01330v2
• Date: Fri, 02 May 2025 04:51:15 GMT
• Title: P-Hologen: An End-to-End Generative Framework for Phase-Only Holograms
• Authors: JooHyun Park, YuJin Jeon, HuiYong Kim, SeungHwan Baek, HyeongYeop Kang,
• Abstract summary: We introduce P-Hologen, the first end-to-end generative framework designed for phase-only holograms (POHs)<n>P-Hologen achieves superior quality and computational efficiency compared to the existing methods.<n>Our model generates high-quality unseen, diverse holographic content without requiring pre-existing images.
• Score: 10.667658956529504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Holography stands at the forefront of visual technology, offering immersive, three-dimensional visualizations through the manipulation of light wave amplitude and phase. Although generative models have been extensively explored in the image domain, their application to holograms remains relatively underexplored due to the inherent complexity of phase learning. Exploiting generative models for holograms offers exciting opportunities for advancing innovation and creativity, such as semantic-aware hologram generation and editing. Currently, the most viable approach for utilizing generative models in the hologram domain involves integrating an image-based generative model with an image-to-hologram conversion model, which comes at the cost of increased computational complexity and inefficiency. To tackle this problem, we introduce P-Hologen, the first end-to-end generative framework designed for phase-only holograms (POHs). P-Hologen employs vector quantized variational autoencoders to capture the complex distributions of POHs. It also integrates the angular spectrum method into the training process, constructing latent spaces for complex phase data using strategies from the image processing domain. Extensive experiments demonstrate that P-Hologen achieves superior quality and computational efficiency compared to the existing methods. Furthermore, our model generates high-quality unseen, diverse holographic content from its learned latent space without requiring pre-existing images. Our work paves the way for new applications and methodologies in holographic content creation, opening a new era in the exploration of generative holographic content. The code for our paper is publicly available on https://github.com/james0223/P-Hologen.

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