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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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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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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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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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Three-dimensional (3D) display using electroholography is a promising technology for next-generation television systems; however, its applicability is limited by the heavy computational load for obtaining computer-generated holograms (CGHs). The CG-line method is an algorithm that calculates CGHs to display 3D line-drawn objects at a very high computational speed but with limited expressiveness; for instance, the intensity along the line must be constant. Herein, we propose an extension for drawing gradated 3D lines using the CG-line method by superimposing phase noise. Consequently, we succeeded in drawing gradated 3D lines while maintaining the high computational speed of the original CG-line method.
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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.
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Point-spread functions (PSFs) are non-stationary signals whose spatial frequency increases with the radius. These signals are only meaningful over a small spatial region when being propagated over short distances and sampled with regular sampling pitch. Otherwise, aliasing at steep incidence angles leads to the computation of spurious frequencies. This is generally addressed by evaluating the PSF in a bounded disk-shaped region, which has the added benefit that it reduces the required number of coefficient updates. This significantly accelerates numerical diffraction calculations in, e.g., wavefront recording planes for high-resolution holograms. However, the use of a disk-shaped PSF is too conservative since it only utilizes about 78.5% of the total bandwidth of the hologram. We therefore derive a novel, to the best of our knowledge, optimally shaped PSF fully utilizing the bandwidth formed by two bounding hyperbola. A number of numerical experiments with the newly proposed pincushion PSF were performed, reporting over three-fold reductions of the signal error and significant improvements to the visual quality of computer-generated holograms at high viewing angles.
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Computer-Generated Holography (CGH) algorithms simulate numerical diffraction, being applied in particular for holographic display technology. Due to the wave-based nature of diffraction, CGH is highly computationally intensive, making it especially challenging for driving high-resolution displays in real-time. To this end, we propose a technique for efficiently calculating holograms of 3D line segments. We express the solutions analytically and devise an efficiently computable approximation suitable for massively parallel computing architectures. The algorithms are implemented on a GPU (with CUDA), and we obtain a 70-fold speedup over the reference point-wise algorithm with almost imperceptible quality loss. We report real-time frame rates for CGH of complex 3D line-drawn objects, and validate the algorithm in both a simulation environment as well as on a holographic display setup.
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The heavy computational burden of computer-generated holograms (CGHs) has been a significant issue for three-dimensional (3D) display systems using electro-holography. Recently, fast CGH calculation methods of line-drawn objects for electro-holography were proposed, which are targeted for holography-based augmented reality/virtual reality devices because of their ability to project object contours in space with a small computational load. However, these methods still face shortcomings, namely, they cannot draw arbitrary curves with graphics processing unit (GPU) acceleration, which is an obstacle for replaying highly expressive and complex 3D images. In this paper, we propose an effective algorithm for calculating arbitrary line-drawn objects at layers of different depths suitable for implementation of GPU. By combining the integral calculation of wave propagation with an algebraic solution, we successfully calculated CGHs of 1, 920 × 1, 080 pixels within 1.1 ms on an NVIDIA Geforce RTX 2080Ti GPU.
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Computer generated holography (CGH) algorithms come in many forms, with different trade-offs in terms of visual quality and calculation speed. However, no CGH algorithm to date can accurately account for all 3D visual cues simultaneously, such as occlusion, shadows, continuous parallax, and precise focal cues, without view discretization. The aim is to create photorealistic CGH content, not only for display purposes but also to create reference data for comparing and testing CGH and compression algorithms. We propose a novel algorithm combining the precision of point-based CGH with the accurate shading and flexibility of ray-tracing algorithms. We demonstrate this by creating a scene with global illumination, soft shadows, and precise occlusion cues, implemented with OptiX and CUDA.
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Holography is a promising technology for photo-realistic three-dimensional (3D) displays because of its ability to replay the light reflected from an object using a spatial light modulator (SLM). However, the enormous computational requirements for calculating computer-generated holograms (CGHs)-which are displayed on an SLM as a diffraction pattern-are a significant problem for practical uses (e.g., for interactive 3D displays for remote navigation systems). Here, we demonstrate an interactive 3D display system using electro-holography that can operate with a consumer's CPU. The proposed system integrates an efficient and fast CGH computation algorithm for line-drawn 3D objects with inter-frame differencing, so that the trajectory of a line-drawn object that is handwritten on a drawing tablet can be played back interactively using only the CPU. In this system, we used an SLM with 1,920 [Formula: see text] 1,080 pixels and a pixel pitch of 8 µm × 8 µm, a drawing tablet as an interface, and an Intel Core i9-9900K 3.60 GHz CPU. Numerical and optical experiments using a dataset of handwritten inputs show that the proposed system is capable of reproducing handwritten 3D images in real time with sufficient interactivity and image quality.
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Digital holography is a promising display technology that can account for all human visual cues, with many potential applications i.a. in AR and VR. However, one of the main challenges in computer generated holography (CGH) needed for driving these displays are the high computational requirements. In this work, we propose a new CGH technique for the efficient analytical computation of lines and arc primitives. We express the solutions analytically by means of incomplete cylindrical functions, and devise an efficiently computable approximation suitable for massively parallel computing architectures. We implement the algorithm on a GPU (with CUDA), provide an error analysis and report real-time frame rates for CGH of complex 3D scenes of line-drawn objects, and validate the algorithm in an optical setup.
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Recently, a calculation method involving sparse point spread functions in the short-time Fourier transform (STFT) domain was proposed. In this paper, a dedicated processor using the STFT algorithm is described, which is implemented on a field-programmable gate array. All the operations in this algorithm are implemented using fixed-point arithmetic. Since this algorithm includes a trigonometric function and an error function, lookup tables (LUTs) are utilized to reduce the calculation costs. We have devised a dedicated circuit architecture that allows parallel operations. In addition, a central processing unit could generate holograms using the STFT-based algorithm with fixed-point arithmetic and LUTs at a higher speed than the generation using floating-point arithmetic.
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Phase-added stereograms are a form of sparse computer generated holograms, subdividing the hologram in small Fourier transformed blocks and updating a single coefficient per block and per point-spread function. Unfortunately, these algorithms' computational performance is often bottlenecked by the relatively high memory requirements. We propose a technique to partition the 3D point cloud into cells using time-frequency analysis, grouping the affected coefficients into subsets that improve caching and minimize memory requirements. This results in significant acceleration of phase added stereogram algorithms without affecting render quality, enabling real-time CGH for driving holographic displays for more complex and detailed scenes than previously possible. We report a 30-fold speedup over the base implementation, achieving real-time speeds of 80ms per million points per megapixel on a single GPU.
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Digital video holography faces two main problems: 1) computer-generation of holograms is computationally very costly, even more when dynamic content is considered; 2) the transmission of many high-resolution holograms requires large bandwidths. Motion compensation algorithms leverage temporal redundancies and can be used to address both issues by predicting future frames from preceding ones. Unfortunately, existing holographic motion compensation methods can only model uniform motions of entire 3D scenes. We address this limitation by proposing both a segmentation scheme for multi-object holograms based on Gabor masks and derive a Gabor mask-based multi-object motion compensation (GMMC) method for the compensation of independently moving objects within a single hologram. The utilized Gabor masks are defined in 4D space-frequency domain (also known as time-frequency domain or optical phase-space). GMMC can segment holograms containing an arbitrary number of mutually occluding objects by means of a coarse triangulation of the scene as side information. We demonstrate high segmentation quality (down to ≤ 0.01% normalized mean-squared error) with Gabor masks for scenes with spatial occlusions. The support of holographic motion compensation for arbitrary multi-object scenes can enable faster generation or improved video compression rates for dynamic digital holography.
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Holographic video requires impractical bitrates for storage and transmission without data compression. We introduce an end-to-end compression pipeline for compressing holographic sequences with known ground truth motion. The compression strategy employs a motion compensation algorithm based on the rotational transformation of an angular spectrum. Residuals arising from the compensation step are represented using short-time Fourier transforms and quantized with uniform mid-rise quantizers whose bit depth is determined by a Lagrangian rate-distortion optimization criterion where the distortion metric is the mean squared error. Experiments use computer-generated holographic videos, and we report Bjøntegaard delta peak signal-to-noise ratio gains of around 20 dB when compared to traditional image/video codecs.