Gaze-Contingent Retinal Speckle Suppression for Perceptually-Matched Foveated Holographic Displays DOI
Praneeth Chakravarthula, Zhan Zhang,

Okan Tarhan Tursun

et al.

IEEE Transactions on Visualization and Computer Graphics, Journal Year: 2021, Volume and Issue: 27(11), P. 4194 - 4203

Published: Aug. 27, 2021

Computer-generated holographic (CGH) displays show great potential and are emerging as the next-generation for augmented virtual reality, automotive heads-up displays. One of critical problems harming wide adoption such is presence speckle noise inherent to holography, that compromises its quality by introducing perceptible artifacts. Although suppression has been an active research area, previous works have not considered perceptual characteristics Human Visual System (HVS), which receives final displayed imagery. However, it well studied sensitivity HVS uniform across visual field, led gaze-contingent rendering schemes maximizing in various computer-generated Inspired this, we present first method reduces "perceived noise" integrating foveal peripheral vision HVS, along with retinal point spread function, into phase hologram computation. Specifically, introduce anatomical statistical receptor distribution our computational optimization, places a higher priority on reducing perceived while being adaptable any individual's optical aberration retina. Our demonstrates superior emulated display. evaluations objective measurements subjective studies demonstrate significant reduction human noise.

Language: Английский

High-speed computer-generated holography using an autoencoder-based deep neural network DOI
Jiachen Wu, Ke‐Xuan Liu, Xiaomeng Sui

et al.

Optics Letters, Journal Year: 2021, Volume and Issue: 46(12), P. 2908 - 2908

Published: May 18, 2021

Learning-based computer-generated holography (CGH) provides a rapid hologram generation approach for holographic displays. Supervised training requires large-scale dataset with target images and corresponding holograms. We propose an autoencoder-based neural network (holoencoder) phase-only generation. Physical diffraction propagation was incorporated into the autoencoder’s decoding part. The holoencoder can automatically learn latent encodings of holograms in unsupervised manner. proposed able to generate high-fidelity 4K resolution 0.15 s. reconstruction results validate good generalizability holoencoder, experiments show fewer speckles reconstructed image compared existing CGH algorithms.

Language: Английский

Citations

163
Probing neural codes with two-photon holographic optogenetics DOI
Hillel Adesnik, Lamiae Abdeladim

Nature Neuroscience, Journal Year: 2021, Volume and Issue: 24(10), P. 1356 - 1366

Published: Aug. 16, 2021

Language: Английский

Citations

126
Roadmap on wavefront shaping and deep imaging in complex media DOI Creative Commons
Sylvain Gigan, Ori Katz, Hilton B. de Aguiar

et al.

Journal of Physics Photonics, Journal Year: 2022, Volume and Issue: 4(4), P. 042501 - 042501

Published: June 8, 2022

The last decade has seen the development of a wide set tools, such as wavefront shaping, computational or fundamental methods, that allow to understand and control light propagation in complex medium, biological tissues multimode fibers. A vibrant diverse community is now working on this field, revolutionized prospect diffraction-limited imaging at depth tissues. This roadmap highlights several key aspects fast developing some challenges opportunities ahead.

Language: Английский

Citations

123
Neural 3D holography DOI
Suyeon Choi, Manu Gopakumar, Yifan Peng

et al.

ACM Transactions on Graphics, Journal Year: 2021, Volume and Issue: 40(6), P. 1 - 12

Published: Dec. 1, 2021

Holographic near-eye displays promise unprecedented capabilities for virtual and augmented reality (VR/AR) systems. The image quality achieved by current holographic displays, however, is limited the wave propagation models used to simulate physical optics. We propose a neural network-parameterized plane-to-multiplane model that closes gap between physics simulation. Our automatically trained using camera feedback it outperforms related techniques in 2D plane-to-plane settings large margin. Moreover, first naturally extend 3D settings, enabling high-quality computer-generated holography novel phase regularization strategy of complex-valued field. efficacy our approach demonstrated through extensive experimental evaluation with both VR optical see-through AR display prototypes.

Language: Английский

Citations

118
Deep learning for digital holography: a review DOI Creative Commons
Tianjiao Zeng, Yanmin Zhu, Edmund Y. Lam

et al.

Optics Express, Journal Year: 2021, Volume and Issue: 29(24), P. 40572 - 40572

Published: Nov. 10, 2021

Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials how can further improve performance and enable new functionalities for DH. Here, we survey recent developments various DH powered by algorithms. This article starts with a brief introduction to holographic imaging, then summarizes most relevant techniques DH, discussions on their benefits challenges. We present case studies covering wide range problems order highlight research achievements date. provide an outlook several promising directions widen use applications.

Language: Английский

Citations

114
End-to-end learning of 3D phase-only holograms for holographic display DOI Creative Commons
Liang Shi, Beichen Li, Wojciech Matusik

et al.

Light Science & Applications, Journal Year: 2022, Volume and Issue: 11(1)

Published: Aug. 3, 2022

Computer-generated holography (CGH) provides volumetric control of coherent wavefront and is fundamental to applications such as 3D displays, lithography, neural photostimulation, optical/acoustic trapping. Recently, deep learning-based methods emerged promising computational paradigms for CGH synthesis that overcome the quality-runtime tradeoff in conventional simulation/optimization-based methods. Yet, quality predicted hologram intrinsically bounded by dataset's quality. Here we introduce a new dataset, MIT-CGH-4K-V2, uses layered depth image data-efficient input two-stage supervised+unsupervised training protocol direct high-quality phase-only holograms. The proposed system also corrects vision aberration, allowing customization end-users. We experimentally show photorealistic holographic projections discuss relevant spatial light modulator calibration procedures. Our method runs real-time on consumer GPU 5 FPS an iPhone 13 Pro, drastically enhanced performance above.

Language: Английский

Citations

100
4K-DMDNet: diffraction model-driven network for 4K computer-generated holography DOI Creative Commons
Ke‐Xuan Liu, Jiachen Wu, Zehao He

et al.

Opto-Electronic Advances, Journal Year: 2023, Volume and Issue: 6(5), P. 220135 - 220135

Published: Jan. 1, 2023

Deep learning offers a novel opportunity to achieve both high-quality and high-speed computer-generated holography (CGH). Current data-driven deep algorithms face the challenge that labeled training datasets limit performance generalization. The model-driven introduces diffraction model into neural network. It eliminates need for dataset has been extensively applied hologram generation. However, existing problem of insufficient constraints. In this study, we propose network capable high-fidelity 4K generation, called Diffraction Model-driven Network (4K-DMDNet). constraint reconstructed images in frequency domain is strengthened. And structure combines residual method sub-pixel convolution built, which effectively enhances fitting ability inverse problems. generalization 4K-DMDNet demonstrated with binary, grayscale 3D images. High-quality full-color optical reconstructions holograms have achieved at wavelengths 450 nm, 520 638 nm.

Language: Английский

Citations

74
The state-of-the-art in computer generated holography for 3D display DOI Creative Commons
David Blinder, Tobias Birnbaum, Tomoyoshi Ito

et al.

Deleted Journal, Journal Year: 2022, Volume and Issue: 3(3), P. 1 - 1

Published: Jan. 1, 2022

Holographic displays have the promise to be ultimate 3D display technology, able account for all visual cues. Recent advances in photonics and electronics gave rise high-resolution holographic prototypes, indicating that they may become widely available near future. One major challenge driving those systems is computational: computer generated holography (CGH) consists of numerically simulating diffraction, which very computationally intensive. Our goal this paper give a broad overview state-of-the-art CGH. We make classification modern CGH algorithms, we describe different algorithmic acceleration techniques, discuss latest dedicated hardware solutions indicate how evaluate perceptual quality summarize our findings, remaining challenges projections on future

Language: Английский

Citations

72
High-contrast, speckle-free, true 3D holography via binary CGH optimization DOI Creative Commons
Byounghyo Lee, Dongyeon Kim, Seungjae Lee

et al.

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Feb. 18, 2022

Holography is a promising approach to implement the three-dimensional (3D) projection beyond present two-dimensional technology. True 3D holography requires abilities of arbitrary volume with high-axial resolution and independent control all voxels. However, it has been challenging true high-reconstruction quality due speckle. Here, we propose practical solution realize speckle-free, high-contrast, by combining random-phase, temporal multiplexing, binary holography, optimization. We adopt random phase for implementation achieve maximum axial fully develop high-performance hologram optimization framework minimize quantization noise, which provides accurate high-contrast reconstructions 2D as well cases. Utilizing fast operation modulation, full-color high-framerate holographic video realized while speckle noise overcome multiplexing. Our high-quality experimentally verified projecting multiple dense images simultaneously. The proposed method can be adopted in various applications where show additional demonstration that realistic VR AR near-eye displays. realization will open new path towards next generation holography.

Language: Английский

Citations

70
Optimizing image quality for holographic near-eye displays with Michelson Holography DOI Creative Commons
Suyeon Choi, Jonghyun Kim, Yifan Peng

et al.

Optica, Journal Year: 2020, Volume and Issue: 8(2), P. 143 - 143

Published: Dec. 10, 2020

We introduce Michelson Holography (MH), a holographic display technology that optimizes image quality for emerging near-eye displays. Using two spatial light modulators, MH is capable of leveraging destructive interference to optically cancel out undiffracted corrupting the observed image. calibrate this system using camera-in-the-loop holography techniques and demonstrate state-of-the-art 2D quality.

Language: Английский

Citations

81