More Like this

1. Proximity Effect Correction for Fresnel Holograms on Nanophotonic Phased Arrays

   https://doi.org/10.1109/VR50410.2021.00058  

   Sun, Xuetong; Zhang, Yang; Huang, Po-Chun; Acharjee, Niloy; Dagenais, Mario; Peckerar, Martin; Varshney, Amitabh (March 2021, 2021 IEEE Virtual Reality and 3D User Interfaces (VR))

   null (Ed.)

   Holographic displays and computer-generated holography offer a unique opportunity in improving optical resolutions and depth characteristics of near-eye displays. The thermally-modulated Nanopho-tonic Phased Array (NPA), a new type of holographic display, affords several advantages, including integrated light source and higher refresh rates, over other holographic display technologies. However, the thermal phase modulation of the NPA makes it susceptible to the thermal proximity effect where heating one pixel affects the temperature of nearby pixels. Proximity effect correction (PEC) methods have been proposed for 2D Fourier holograms in the far field but not for Fresnel holograms at user-specified depths. Here we extend an existing PEC method for the NPA to Fresnel holograms with phase-only hologram optimization and validate it through computational simulations. Our method is not only effective in correcting the proximity effect for the Fresnel holograms of 2D images at desired depths but can also leverage the fast refresh rate of the NPA to display 3D scenes with time-division multiplexing. 

   more » « less
2. Realtime bacteria detection and analysis in sterile liquid products using deep learning holographic imaging

   https://doi.org/10.1038/s44328-024-00008-9  

   Bravo-Frank, Nicholas; Zende, Rushikesh; Feng, Lei; Mesyngier, Nicolas; Pachpute, Aditya; Hong, Jiarong (December 2024, npj Biosensing)

   Abstract We introduce a digital inline holography (DIH) method combined with deep learning (DL) for real-time detection and analysis of bacteria in liquid suspension. Specifically, we designed a prototype that integrates DIH with fluorescence imaging to efficiently capture holograms of bacteria flowing in a microfluidic channel, utilizing the fluorescent signal to manually identify ground truths for validation. We process holograms using a tailored DL framework that includes preprocessing, detection, and classification stages involving three specific DL models trained on an extensive dataset that included holograms of generic particles present in sterile liquid and five bacterial species featuring distinct morphologies, Gram stain attributes, and viability. Our approach, validated through experiments with synthetic data and sterile liquid spiked with different bacteria, accurately distinguishes between bacteria and particles, live and dead bacteria, and Gram-positive and negative bacteria of similar morphology, all while minimizing false positives. The study highlights the potential of combining DIH with DL as a transformative tool for rapid bacterial analysis in clinical and industrial settings, with potential extension to other applications including pharmaceutical screening, environmental monitoring, and disease diagnostics. 

   more » « less
3. Stochastic Light Field Holography

   https://doi.org/10.1109/ICCP56744.2023.10233716  

   Schiffers, Florian; Chakravarthula, Praneeth; Matsuda, Nathan; Kuo, Grace; Tseng, Ethan; Lanman, Douglas; Heide, Felix; Cossairt, Oliver (July 2023, 2023 IEEE International Conference on Computational Photography (ICCP))

   The Visual Turing Test is the ultimate goal to evaluate the realism of holographic displays. Previous studies have focused on addressing challenges such as limited e ́tendue and image quality over a large focal volume, but they have not investigated the effect of pupil sampling on the viewing experience in full 3D holograms. In this work, we tackle this problem with a novel hologram generation algorithm motivated by matching the projection operators of incoherent (Light Field) and coherent (Wigner Function) light transport. To this end, we supervise hologram computation using synthesized photographs, which are rendered on-the-fly using Light Field refocusing from stochastically sampled pupil states during optimization. The proposed method produces holograms with correct parallax and focus cues, which are important for passing the Visual Turing Test. We validate that our approach compares favorably to state-of-the-art CGH algorithms that use Light Field and Focal Stack supervision. Our experiments demonstrate that our algorithm improves the viewing experience when evaluated under a large variety of different pupil states. 

   more » « less
4. Hogel-Free Holography

   https://doi.org/10.1145/3516428  

   Chakravarthula, Praneeth; Tseng, Ethan; Fuchs, Henry; Heide, Felix (October 2022, ACM Transactions on Graphics)

   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. 

   more » « less
5. View synthesis for 3D computer-generated holograms using deep neural fields

   https://doi.org/10.1364/OE.559364  

   Chen, Kenneth; Wen, Anzhou; Zhang, Yunxiang; Chakravarthula, Praneeth; Sun, Qi (April 2025, Optics Express)

   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. 

   more » « less