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As an important three-dimensional (3D) display technology, computer-generated holograms (CGHs) have been facing challenges of computational efficiency and realism. The polygon-based method, as the mainstream CGH algorithm, has been widely studied and improved over the past 20 years. However, few comprehensive and high-speed methods have been proposed. In this study, we propose an analytical spectrum method based on the principle of spectral energy concentration, which can achieve a speedup of nearly 30 times and generate high-resolution (8 K) holograms with low memory requirements; Based on the Phong illumination model and the sub-triangles method, we propose a shading rendering algorithm to achieve a very smooth and realistic reconstruction with only a small increase in computational effort. Benefiting from the idea of triangular subdivision and octree structures, the proposed original occlusion culling scheme can closely crop the overlapping areas with almost no additional overhead, thus rendering a 3D parallax sense. With this, we built a comprehensive high-speed rendering pipeline of polygon-based holograms capable of computing any complex 3D object. Numerical and optical reconstructions confirmed the generalizability of the pipeline.

In this paper, we have proposed a hologram calculation method using polynomial approximations for reducing the computational complexity of point-cloud-based hologram calculations. The computational complexity of existing point-cloud-based hologram calculations is proportional to the product of the number of point light sources and hologram resolution, whereas that of the proposed method can be reduced to approximately proportional to the sum of the number of point light sources and hologram resolution by approximating the object wave with polynomials. The computation time and reconstructed image quality were compared with those of the existing methods. The proposed method was approximately 10 times faster than the conventional acceleration method, and did not produce significant errors when the object was far from the hologram.

We propose a fast layer hologram calculation method from RGB-D images by compressing both layer images and point-spread functions using the wavelet transform and investigate the calculation speed and the reconstructed image quality through simulation.

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.

Holography is a technology that uses light interference and diffraction to record and reproduce three-dimensional (3D) information. Using computers, holographic 3D scenes (electroholography) have been widely studied. Nevertheless, its practical application requires enormous computing power, and current computers have limitations in real-time processing. In this study, we show that holographic reconstruction (HORN)-9, a special-purpose computer for electroholography with the Hilbert transform, can compute a 1, 920 × 1, 080-pixel computer-generated hologram from a point cloud of 65,000 points in 0.030 s (33 fps) on a single card. This performance is 8, 7, and 170 times more efficient than a previously developed HORN-8, a graphics processing unit, and a central processing unit (CPU), respectively. We also demonstrated the real-time processing and display of 400,000 points on multiple HORN-9s, achieving an acceleration of 600 times with four HORN-9 units compared with a single CPU.

Scaled binary ghost imaging has a challenge for high calculation amounts. In this study, we implement a scaled binary ghost imaging into a field-programmable gate array to accelerate the processing time.

High-order Gaussian beams with multiple propagation modes have been studied for free-space optical communications. Fast classification of beams using a diffractive deep neural network (D2NN) has been proposed. D2NN optimization is important because it has numerous hyperparameters, such as interlayer distances and mode combinations. In this study, we classify Hermite–Gaussian beams, which are high-order Gaussian beams, using a D2NN, and automatically tune one of its hyperparameters known as the interlayer distance. We used the tree-structured Parzen estimator, a hyperparameter auto-tuning algorithm, to search for the best model. As a result, the proposed method improved the classification accuracy in a 16 mode classification from 98.3% in the case of equal spacing of layers to 98.8%. In a 36 mode classification, the proposed method significantly improved the classification accuracy from 84.9% to 94.9%. In addition, we confirmed that accuracy by auto-tuning improves as the number of classification modes increases.

We developed dedicated processors for holography named ‘HORN,” which accelerate hologram calculation by parallelizing many calculation units. If we reduce the calculation accuracy of the units, the circuit size of the units decreases, which in turn leads to an increase in the number of the units; however, owing to this the image quality degrades. In this study, we propose a dedicated processor with low accuracy units assisted by a deep neural network (DNN). After performing the hologram calculation in the processor, we restore a high accuracy hologram from the low accuracy one using the DNN.

Layer-based hologram calculations generate holograms from RGB and depth images by repeating diffraction calculations using complex Fourier transforms (FTs). Holograms generated as such are suitable for near-eye display and can be easily reconstructed with good image quality, but they are computationally expensive because of multiple complex-valued operations, including complex FTs. In this study, we propose an acceleration method for layer-based hologram calculations by reducing time-consuming complex-valued operations using the real-valued FT and Hartley transform as real linear transformations. Real linear transformations transform real input data to real output data; thus, the proposed method generates amplitude holograms. Thus, we also propose a technique to convert holograms generated by real linear transformations into phase-only holograms using the half-zone plate process and digitalized single-sideband method while maintaining the calculation acceleration. The proposed method can speed up hologram calculations by a factor of around three while maintaining the same image quality as the conventional method.

Computational holography, encompassing computer-generated holograms and digital holography, utilizes diffraction calculations based on complex-valued operations and complex Fourier transforms. However, for some holographic applications, only real-valued holograms or real-valued diffracted results are required. This study proposes a real-valued diffraction calculation that does not require any complex-valued operation. Instead of complex-valued Fourier transforms, we employ a pure real-valued transform. Among the several real-valued transformations that have been proposed, we employ the Hartley transformation. However, our proposed method is not limited to this transformation, as other real-valued transformations can be utilized.

Holograms are computed by superimposing point spread functions (PSFs), which represent the distribution of light on the hologram plane. The computational cost and the spatial bandwidth product required to generate holograms are significant; therefore, it is challenging to compute high-resolution holograms at the rates required for videos. Among the possible displays, fixed-eye-position holographic displays, such as holographic head-mounted displays, reduce the spatial bandwidth product by fixing eye positions while satisfying almost all human depth cues. In eye-fixed holograms, by calculating a part distribution of the entire PSF, we observe reconstructed images that maintain the image quality and the depth of focus almost as high as those generated by the entire PSF. In this study, we accelerate the calculation of eye-fixed holograms by engineering the PSFs. We propose cross and radial PSFs, and we determine that, out of the two, the radial PSFs have a better image quality. By combining the look-up table method and the wavefront-recording plane method with radial PSFs, we show that the proposed method can rapidly compute holograms.

Fourier transform-based diffraction calculations are essential for computational optics. However, the diffraction calculations can be corrupted by the introduction of strong ringing artifacts due to the introduction of zero-padding to avoid circular convolution or to control the sampling intervals. We propose a simple de-ringing method using average subtractions for application to on-axis and off-axis diffraction calculations. To verify the effectiveness of the proposed method, we compared the diffracted fields obtained using zero-padding, a flat-top window method, a mirror expansion method, and the whole and border average subtractions proposed. Furthermore, we confirmed the effectiveness of the proposed method for hologram calculations using double phase encoding and image reconstructions of inline digital holography.

In this study, we proposed a hologram calculation method for light-in-flight recording by holography (LIF holography). First, we simulated the behavior of ultrashort pulsed light on the diffuser plate using the two-dimensional finite-difference time-domain method. Second, we calculated the light propagation from the diffuser plate to the recording material and generated a hologram based on the calculation model of LIF holography. We reconstructed moving pictures of pulsed-light propagation from the calculated hologram. Because the behavior of the pulsed light in the moving pictures agreed well with that obtained from the reported optical experiment, the proposed method was successfully validated.

Electroholography can produce natural 3D scenes and has gained recognition as an ideal 3D technology. However, insufficient computational power has made it difficult to achieve real-time electroholography. In this paper, we developed a compact special-purpose system for calculating phase-only holograms. We implemented the developed system using a system on a chip embedded with a processor and logic circuit blocks. Our system successfully computed holograms of 1,920×1,080 pixels from a point-cloud with 32,500 points at 10 frames per second. The system is 147 times faster than a personal computer (with 6 CPU cores).

Single-pixel imaging allows for high-speed imaging, miniaturization of optical systems, and imaging over a broad wavelength range, which is difficult by conventional imaging sensors, such as pixel arrays. However, a challenge in single-pixel imaging is low image quality in the presence of undersampling. Deep learning is an effective method for solving this challenge; however, a large amount of memory is required for the internal parameters. In this study, we propose single-pixel imaging based on a recurrent neural network. The proposed approach succeeds in reducing the internal parameters, reconstructing images with higher quality, and showing robustness to noise.

This study aims to improve the image quality of holographic three-dimensional (3-D) images based on the wavefront recording plane (WRP) method. In this method, we place a WRP close to the 3-D objects to reduce the propagation distance of light from the objects to the WRP. The conventional WRP method has been implemented only under conditions that did not cause aliasing noise. This study proposes a WRP method with a limiting diffraction region from the WRP to the hologram such that we can perform the WRP method under any condition. As a result, we succeeded in improving the image quality of the 3-D images based on the WRP method.

We propose an indirect method for generating a complex hologram and phase-only hologram from an amplitude hologram using the Hilbert transform. The Hilbert transform generates an imaginary part of complex amplitude from only an amplitude hologram, resulting in the reduction of the total computational complexity of complex and phase-only holograms. More importantly, the proposed method can reduce the hardware resources of dedicated hologram processors.

In this study, we compare the ray-tracing method with the look-up table (LUT) method in order to optimize computer-generated hologram (CGH) calculation based on the wavefront recording plane (WRP) method. The speed of the WRP-based CGH calculation largely depends on implementation factors, such as calculation methods, hardware, and parallelization method. Therefore, we evaluated the calculation time and image quality of the reconstructed three-dimensional (3D) image by using the ray-tracing and LUT methods in the central processing unit (CPU) and graphics processing unit (GPU) implementations. Thereafter, we performed several implementations by changing the number of object points and the distance from 3D objects to the WRP. Furthermore, we confirmed different characteristics between CPU and GPU implementations.

We propose a phase retrieval method using axial diffraction patterns under planar and spherical wave illuminations. The proposed method uses a ptychographic iterative engine (PIE) for the phase retrieval algorithm. The proposed approach uses multiple diffraction patterns. Thus, adjusting the alignment of each diffraction pattern is mandatory, and we propose a method to adjust the alignment. In addition, a random selection of the measured diffraction patterns is used to further accelerate the convergence of the PIE-based optimization. To confirm the effectiveness of the proposed method, we compare the conventional and proposed methods using a simulation and optical experiments.