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Computer-generated holography (CGH) holds transformative potential for a wide range of applications, including direct-view, virtual and augmented reality, and automotive display systems. While research on holographic displays has recently made impressive progress, image quality and eye safety of holographic displays are fundamentally limited by the speckle introduced by coherent light sources. Here, we develop an approach to CGH using partially coherent sources. For this purpose, we devise a wave propagation model for partially coherent light that is demonstrated in conjunction with a camera-in-the-loop calibration strategy. We evaluate this algorithm using light-emitting diodes (LEDs) and superluminescent LEDs (SLEDs) and demonstrate improved speckle characteristics of the resulting holograms compared with coherent lasers. SLEDs in particular are demonstrated to be promising light sources for holographic display applications, because of their potential to generate sharp and high-contrast two-dimensional (2D) and 3D images that are bright, eye safe, and almost free of speckle.
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View synthesis for 3D computer-generated holograms using deep neural fields
Computer-generated holography (CGH) simulates the propagation and interference of complex light waves, allowing it to reconstruct realistic images captured from a specific viewpoint by solving the corresponding Maxwell equations. However, in applications such as virtual and augmented reality, viewers should freely observe holograms from arbitrary viewpoints, much as how we naturally see the physical world. In this work, we train a neural network to generate holograms at any view in a scene. Our result is the Neural Holographic Field: the first artificial-neural-network-based representation for light wave propagation in free space and transform sparse 2D photos into holograms that are not only 3D but also freely viewable from any perspective. We demonstrate by visualizing various smartphone-captured scenes from arbitrary six-degree-of-freedom viewpoints on a prototype holographic display. To this end, we encode the measured light intensity from photos into a neural network representation of underlying wavefields. Our method implicitly learns the amplitude and phase surrogates of the underlying incoherent light waves under coherent light display conditions. During playback, the learned model predicts the underlying continuous complex wavefront propagating to arbitrary views to generate holograms.
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- Award ID(s):
- 2225861
- PAR ID:
- 10585270
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Express
- Volume:
- 33
- Issue:
- 9
- ISSN:
- 1094-4087; OPEXFF
- Format(s):
- Medium: X Size: Article No. 19399
- Size(s):
- Article No. 19399
- Sponsoring Org:
- National Science Foundation
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