High-speed computer-generated holography using an autoencoder-based deep neural network

Optics Letters, Год журнала: 2021, Номер 46(12), С. 2908 - 2908

Опубликована: Май 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.

Язык: Английский

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Probing neural codes with two-photon holographic optogenetics

Nature Neuroscience, Год журнала: 2021, Номер 24(10), С. 1356 - 1366

Опубликована: Авг. 16, 2021

Язык: Английский

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Roadmap on wavefront shaping and deep imaging in complex media

Journal of Physics Photonics, Год журнала: 2022, Номер 4(4), С. 042501 - 042501

Опубликована: Июнь 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.

Язык: Английский

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Neural 3D holography

ACM Transactions on Graphics, Год журнала: 2021, Номер 40(6), С. 1 - 12

Опубликована: Дек. 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.

Язык: Английский

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Deep learning for digital holography: a review

Optics Express, Год журнала: 2021, Номер 29(24), С. 40572 - 40572

Опубликована: Ноя. 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.

Язык: Английский

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End-to-end learning of 3D phase-only holograms for holographic display

Light Science & Applications, Год журнала: 2022, Номер 11(1)

Опубликована: Авг. 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.

Язык: Английский

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4K-DMDNet: diffraction model-driven network for 4K computer-generated holography

Opto-Electronic Advances, Год журнала: 2023, Номер 6(5), С. 220135 - 220135

Опубликована: Янв. 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.

Язык: Английский

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The state-of-the-art in computer generated holography for 3D display

Deleted Journal, Год журнала: 2022, Номер 3(3), С. 1 - 1

Опубликована: Янв. 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

Язык: Английский

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High-contrast, speckle-free, true 3D holography via binary CGH optimization

Scientific Reports, Год журнала: 2022, Номер 12(1)

Опубликована: Фев. 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.

Язык: Английский

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Optimizing image quality for holographic near-eye displays with Michelson Holography

Optica, Год журнала: 2020, Номер 8(2), С. 143 - 143

Опубликована: Дек. 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.

Язык: Английский

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