Computer-generated holograms (CGHs) are a key technology in electroholography systems; however, heavy calculations are required to calculate CGHs. We review fast calculation techniques for CGH calculation of a point-light-source-based model, which is a simple and general model of a three-dimensional object in an electroholography system. To reduce the calculation time, many methods that reduce the temporal and spatial redundancy of the CGH calculation have been developed (e.g., look-up table method, the wavefront recording plane method, and other approximation techniques). The implementation of such methods on parallel computers (e.g., graphic processing unit and field programmable gate arrays) has also been reported.

This paper proposes a particle volume reconstruction directly from an in-line hologram using a deep neural network (DNN). Digital holographic volume reconstruction conventionally uses multiple diffraction calculations to obtain sectional reconstructed images from an in-line hologram, followed by detection of the lateral and axial positions, and the sizes of particles by using focus metrics. However, the axial resolution is limited by the numerical aperture of the optical system, and the processes are time consuming. The method proposed here can simultaneously detect the lateral and axial positions, and the particle sizes via a DNN. We numerically investigated the performance of the DNN in terms of the errors in the detected positions and sizes. The calculation time is faster than conventional diffracted-based approaches.

Digital holography allows production of high-speed three-dimensional images at rates over 100,000 frames per second; however, simultaneously obtaining suitable performance and levels of accuracy using digital holography is difficult. This problem prevents high-speed three-dimensional imaging from being used for vibrometry. In this paper, we propose and test a digital holography method that can produce vibration measurements. The method is based on single-shot phase-shifting interferometry. Herein, we imaged the surface of a loudspeaker diaphragm and measured its displacement due to the vibrations produced by a frequency sweep signal. We then analyzed the frequency of the experimental data and confirmed that the frequency spectra inferred from the reconstructed images agreed well with the spectra produced by the sound recorded by a microphone. This method can be used for measuring vibrations with three-dimensional imaging for loudspeakers, microelectromechanical systems, surface acoustic wave filters, and biological tissues and organs.

Electro-holography is a promising display technology that can reconstruct a photorealistic three-dimensional (3D) movie; however, it is yet to be realized practically owing to the need for enormous calculation power. A special-purpose computer for electro-holography, namely HORN, has been studied for over 20 years as a means to solve this problem. The latest version of HORN, HORN-8, was developed using field programmable gate array (FPGA) technology. Initially, a circuit for amplitude-type electro-holography was implemented in HORN-8; however, implementation of phase-type electro-holography has remained an issue. In this paper, the development of new version of HORN-8 and its cluster system, which achieved a real-time reconstruction of a 3D movie with point clouds comprised of 32,000 points for phase-type electro-holography, was reported.

We developed a HORN-8 system that generates computer-generated holograms at a high speed. The cluster system employed eight HORN-8 boards and achieved a level of performance that was 1,000 times faster than that of a PC. From a point-cloud model comprising 65,536 ($2^{16}$) points, the proposed cluster system can update a 2-million-pixel (1,920 × 1,080) hologram at 60 frames per second. 65,536 ($2^{16}$) is the internal memory size of the HORN-8 hardware. However, the HORN-8 system can calculate a hologram at a high speed even if the number of point-cloud sources exceeds 65,536 ($2^{16}$). Herein, we spatiotemporally divided a point-cloud model comprising ~400,000 points and succeeded in reproducing the video-holography. We demonstrated the performance of the special-purpose computer for electroholography using HORN-8 hardware that does not require a large internal memory when the calculation speed is high.

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.