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Holograms can be observed from different viewpoints, because light waves can be encoded to propagate in multiple directions. Thus, accurate holograms for 3D display should model viewpoint-dependent light reflections. We proposed a new, to the best of our knowledge, hologram generation method for objects represented by polygonal meshes, whose lighting changes as the viewer moves, all while rendering smooth shading using low-poly objects. The proposed method leverages bump mapping and converts it into a bump-phase map encoding the propagation frequency and then spreads the reflected light wave so that only a specific viewpoint can receive them. Simulation experiments with small pixel pitches confirm the method's high computational performance.
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Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography. Supplementary Information: The online version contains supplementary material available at 10.1007/s00340-024-08280-3.
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A novel scanning particle image velocimetry technique, to the best of our knowledge, is proposed to characterize flows in microfluidic applications. Three-dimensional information is acquired by oscillating the target sample over a fixed focal plane, allowing the reconstruction of particle trajectories with micrometer accuracy over an extended depth. This technology is suited for investigating acoustic flows with unprecedented precision in microfluidic applications. In this contribution, we describe the experimental setup and the data processing pipeline in detail; we study the technique's performance by reconstructing pressure-driven flow; and we report the three-dimensional trajectory of a 2 µm particle in an acoustic flow in a 525µm×375µm microchannel with micrometric accuracy.
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Electro-holography is a promising 3D display technology, as it can, in principle, account for all visual cues. Computing the interference patterns to drive them is highly calculation-intensive, requiring the design and development of efficient computer-generated holography (CGH) algorithms to facilitate real-time display. In this work, we propose a new algorithm for computing the CGH for arbitrary 3D curves using splines, as opposed to previous solutions, which could only draw planar curves. The solutions are analytically expressed; we conceived an efficiently computable approximation suitable for GPU implementations. We report over 55-fold speedups over the reference point-wise algorithm, resulting in real-time 4K holographic video generation of complex 3D curved objects. The proposed algorithm is validated numerically and optically on a holographic display setup.
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The wavefront recording plane (WRP) method is an algorithm for computer-generated holograms, which has significantly promoted the accelerated computation of point-based holograms. Similarly, in this paper, we propose a WRP-like method for polygon-based holograms. A WRP is placed near the object, and the diffracted fields of all polygons are aggregated in the WRP so that the fields propagating from the polygonal mesh affect only a small region of the plane rather than the full region. Unlike the conventional WRP method used in point-based holograms, the proposed WRP-like method utilizes sparse sampling in the frequency domain to significantly reduce the practical computational kernel size. The proposed WRP-like method and the analytical shading model are used to generate polygon-based holograms of multiple three-dimensional (3D) objects, which are then reproduced to confirm 3D perception. The results indicate that the proposed WRP-like method based on an analytical algorithm is hundreds of times faster than the reference full region sampling case; a hologram with tens of thousands of triangles can be computed in seconds even on a CPU, whereas previous methods required a graphics processing unit to achieve these speeds.
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The large number of pixels to be processed and stored for digital holographic techniques necessitates the development of effective lossless compression techniques. Use cases for such techniques are archiving holograms, especially sensitive biomedical data, and improving the data transmission capacity of bandwidth-limited data transport channels where quality loss cannot be tolerated, like display interfaces. Only a few lossless compression techniques exist for holography, and the search for an efficient technique well suited for processing the large amounts of pixels typically encountered is ongoing. We demonstrate the suitability of autoregressive modeling for compressing signals with limited spatial bandwidth content, like holographic images. The applicability of such schemes for any such bandlimited signal is motivated by a mathematical insight that is novel to our knowledge. The devised compression scheme is lossless and enables decoding architecture that essentially has only two steps. It is also highly scalable, with smaller model sizes providing an effective, low-complexity mechanism to transmit holographic data, while larger models obtain significantly higher compression ratios when compared to state-of-the-art lossless image compression solutions, for a wide selection of both computer-generated and optically-acquired holograms. We also provide a detailed analysis of the various methods that can be used for determining the autoregressive model in the context of compression.
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Computer-generated holograms (CGHs) are usually calculated from point clouds or polygon meshes. Point-based holograms are good at depicting details of objects, such as continuous depth cues, while polygon-based holograms tend to efficiently render high-density surfaces with accurate occlusions. Herein, we propose a novel point-polygon hybrid method (PPHM) to compute CGHs for the first time (to the best of our knowledge), which takes advantage of both point-based and polygon-based methods, and thus performs better than each of them separately. Reconstructions of 3D object holograms confirm that the proposed PPHM can present continuous depth cues with fewer triangles, implying high computational efficiency without losing quality.
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Digital reconstructions of numerical holograms enable data visualization and serve a multitude of purposes ranging from microscopy to holographic displays. Over the years, many pipelines have been developed for specific hologram types. Within the standardization effort of JPEG Pleno holography, an open-source MATLAB toolbox was developed that reflects the best current consensus. It can process Fresnel, angular spectrum, and Fourier-Fresnel holograms with one or more color channels; it also allows for diffraction-limited numerical reconstructions. The latter provides a way to reconstruct holograms at their intrinsic physical instead of an arbitrarily chosen numerical resolution. The Numerical Reconstruction Software for Holograms v10 supports all large public data sets featured by UBI, BCOM, ETRI, and ETRO, in their native and vertical off-axis binary forms. Through the release of this software, we hope to improve the reproducibility of research, thus enabling consistent comparison of data between research groups and the quality of specific numerical reconstructions.
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We propose a deep hologram converter based on deep learning to convert low-precision holograms into middle-precision holograms. The low-precision holograms were calculated using a shorter bit width. It can increase the amount of data packing for single instruction/multiple data in the software approach and the number of calculation circuits in the hardware approach. One small and one large deep neural network (DNN) are investigated. The large DNN exhibited better image quality, whereas the smaller DNN exhibited a faster inference time. Although the study demonstrated the effectiveness of point-cloud hologram calculations, this scheme could be extended to various other hologram calculation algorithms.
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With holographic displays requiring giga- or terapixel resolutions, data compression is of utmost importance in making holography a viable technique in the near future. In addition, since the first-generation of holographic displays is expected to require binary holograms, associated compression algorithms are expected to be able to handle this binary format. In this work, the suitability of a context based Bayesian tree model is proposed as an extension to adaptive binary arithmetic coding to facilitate the efficient lossless compression of binary holograms. In addition, we propose a quadtree-based adaptive spatial segmentation strategy, as the scale dependent, quasi-stationary behavior of a hologram limits the applicability of the advocated modelling approach straightforwardly on the full hologram. On average, the proposed compression strategy produces files that are around 12% smaller than JBIG2, the reference binary image codec.