Holography is a promising avenue for high-quality displays without requiring bulky, complex optical systems. While recent work has demonstrated accurate hologram generation of 2D scenes, high-quality holographic projections of 3D scenes has been out of reach until now. Existing multiplane 3D holography approaches fail to model wavefronts in the presence of partial occlusion while holographic stereogram methods have to make a fundamental tradeoff between spatial and angular resolution. In addition, existing 3D holographic display methods rely on heuristic encoding of complex amplitude into phase-only pixels which results in holograms with severe artifacts. Fundamental limitations of the input representation, wavefront modeling, and optimization methods prohibit artifact-free 3D holographic projections in today’s displays. To lift these limitations, we introduce hogel-free holography which optimizes for true 3D holograms, supporting both depth- and view-dependent effects for the first time. Our approach overcomes the fundamental spatio-angular resolution tradeoff typical to stereogram approaches. Moreover, it avoids heuristic encoding schemes to achieve high image fidelity over a 3D volume. We validate that the proposed method achieves 10 dB PSNR improvement on simulated holographic reconstructions. We also validate our approach on an experimental prototype with accurate parallax and depth focus effects.
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Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram
Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram. However, unlike a conventional bright-field microscopy image, the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane objects. Here, we demonstrate that cross-modality deep learning using a generative adversarial network (GAN) can endow holographic images of a sample volume with bright-field microscopy contrast, combining the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of incoherent bright-field microscopy. We illustrate the performance of this “bright-field holography” method through the snapshot imaging of bioaerosols distributed in 3D, matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope. This data-driven deep-learning-based imaging method bridges the contrast gap between coherent and incoherent imaging, and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram, benefiting from the wave-propagation framework of holography.
more » « less- PAR ID:
- 10153484
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Light: Science & Applications
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2047-7538
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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