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

arXiv: https://arxiv.org/abs/2404.01330 · HTML: https://mlsanity.com/paper/2404.01330/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)
- [Implicit Diffusion Models for Continuous Super-Resolution](https://mlsanity.com/paper/2303.16491/) (2023)
- [PixelSNAIL: An Improved Autoregressive Generative Model](https://mlsanity.com/paper/1712.09763/) (2017)
- [High-Fidelity Guided Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2211.17084/) (2022)
- [SROBB: Targeted Perceptual Loss for Single Image Super-Resolution](https://mlsanity.com/paper/1908.07222/) (2019)
- [PhaseGAN: A deep-learning phase-retrieval approach for unpaired datasets](https://mlsanity.com/paper/2011.08660/) (2020)
- [Neural 'Etendue Expander for Ultra-Wide-Angle High-Fidelity Holographic Display](https://mlsanity.com/paper/2109.08123/) (2021)
- [Learned holographic light transport](https://mlsanity.com/paper/2108.08253/) (2021)
- [Encoding complex fields by using a phase-only optical element: mitigation of pixel crosstalk effects](https://mlsanity.com/paper/1903.06046/) (2019)
- [Compression of phase-only holograms with JPEG standard and deep learning](https://mlsanity.com/paper/1806.03811/) (2018)

## Abstract (arXiv)

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.