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1. Holographic Applications in STEM Pedagogy and Training

   Flowers, L (July 2024, International journal of science and research)

   Three-dimensional (3D) holographic technology, like virtual, augmented, and mixed reality technology, is an emerging technology designed to improve learning outcomes in science, technology, engineering, and math (STEM) disciplines. Holograms provide unique opportunities to enhance students’ understanding of intractable concepts and processes using engaging visualization methods. Portable 3D holographic fans allow for the improved visualization of molecules, structures, pathways, and other STEM - related content that have the potential to elevate information acquisition in novel ways that extend beyond 2-D presentations and textbook figures. While the potentiality of this innovative technology is exciting, adopting 3D holographic materials in the STEM pedagogical and research environment requires producing literary evidence to justify usage in specific contexts and sufficient guidance on safety protocols. A review of 3D hologram technology revealed an inadequate amount of efficacy research. Quantitative and qualitative research studies involving STEM majors, faculty, and researchers constitute the engine that will drive the utilization of 3D hologram visualizations in STEM undergraduate, graduate, and professional school classrooms and laboratories. The current article reviews relevant research findings and discusses the potential impacts of 3D hologram technology in teaching, research, distance learning, and medical contexts. 

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2. Instant-SFH: Non-Iterative Sparse Fourier Holograms Using Perlin Noise

   https://doi.org/10.3390/s24227358  

   Li, David; Jabbireddy, Susmija; Zhang, Yang; Metzler, Christopher; Varshney, Amitabh (November 2024, Sensors)

   Holographic displays are an upcoming technology for AR and VR applications, with the ability to show 3D content with accurate depth cues, including accommodation and motion parallax. Recent research reveals that only a fraction of holographic pixels are needed to display images with high fidelity, improving energy efficiency in future holographic displays. However, the existing iterative method for computing sparse amplitude and phase layouts does not run in real time; instead, it takes hundreds of milliseconds to render an image into a sparse hologram. In this paper, we present a non-iterative amplitude and phase computation for sparse Fourier holograms that uses Perlin noise in the image–plane phase. We conduct simulated and optical experiments. Compared to the Gaussian-weighted Gerchberg–Saxton method, our method achieves a run time improvement of over 600 times while producing a nearly equal PSNR and SSIM quality. The real-time performance of our method enables the presentation of dynamic content crucial to AR and VR applications, such as video streaming and interactive visualization, on holographic displays. 

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3. Computing high quality phase-only holograms for holographic displays

   https://doi.org/10.1117/12.2547647  

   Chakravarthula, Praneeth Kumar; Peng, Yifan; Kollin, Joel; Heide, Felix; Fuchs, Henry (February 2020, Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR), International Society for Optics and Photonics, 2020)

   Holography has demonstrated potential to achieve a wide field of view, focus supporting, optical see-through augmented reality display in an eyeglasses form factor. Although phase modulating spatial light modulators are becoming available, the phase-only hologram generation algorithms are still imprecise resulting in severe artifacts in the reconstructed imagery. Since the holographic phase retrieval problem is non-linear and non-convex and computationally expensive with the solutions being non-unique, the existing methods make several assumptions to make the phase-only hologram computation tractable. In this work, we deviate from any such approximations and solve the holographic phase retrieval problem as a quadratic problem using complex Wirtinger gradients and standard first-order optimization methods. Our approach results in high-quality phase hologram generation with at least an order of magnitude improvement over existing state-of-the-art approaches. 

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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. 

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5. Machine learning enables precise holographic characterization of colloidal materials in real time

   https://doi.org/10.1039/D2SM01283A  

   Altman, Lauren E.; Grier, David G. (April 2023, Soft Matter)

   Holographic particle characterization uses in-line holographic video microscopy to track and characterize individual colloidal particles dispersed in their native fluid media. Applications range from fundamental research in statistical physics to product development in biopharmaceuticals and medical diagnostic testing. The information encoded in a hologram can be extracted by fitting to a generative model based on the Lorenz–Mie theory of light scattering. Treating hologram analysis as a high-dimensional inverse problem has been exceptionally successful, with conventional optimization algorithms yielding nanometer precision for a typical particle's position and part-per-thousand precision for its size and index of refraction. Machine learning previously has been used to automate holographic particle characterization by detecting features of interest in multi-particle holograms and estimating the particles' positions and properties for subsequent refinement. This study presents an updated end-to-end neural-network solution called CATCH (Characterizing and Tracking Colloids Holographically) whose predictions are fast, precise, and accurate enough for many real-world high-throughput applications and can reliably bootstrap conventional optimization algorithms for the most demanding applications. The ability of CATCH to learn a representation of Lorenz–Mie theory that fits within a diminutive 200 kB hints at the possibility of developing a greatly simplified formulation of light scattering by small objects. 

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