Reducing the computational complexity of high-resolution hologram calculations using polynomial approximation.

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.

Deep hologram converter from low-precision to middle-precision holograms.

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.

Multiple motion picture recording in light-in-flight recording by holography with an angular multiplexing technique.

Light-in-flight recording by holography (LIF holography) is an ultrafast imaging technique for recording light pulse propagation as a motion picture. In this study, we propose and demonstrate multiple motion picture recordings of light pulse propagation by use of LIF holography with angular multiplexing. We set incident angles of reference light pulses to remove the difficulty in adjusting the optical path length difference between an object light pulse and reference light pulses and the complexity of the optical system. In the experiment, by using LIF holography with angular multiplexing, we succeeded in recording a propagating light pulse as two motion pictures with durations of 129.6 ps without an inseparable superimposition of the reconstructed images. In addition, cross talk between the recorded images, noise caused by cross-terms in an image plane, and the number of motion pictures that can be recorded are discussed.

Variable-intensity line 3D images drawn using kinoform-type electroholography superimposed with phase error.

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.

Single-pixel imaging for edge images using deep neural networks.

Edge images are often used in computer vision, cellular morphology, and surveillance cameras, and are sufficient to identify the type of object. Single-pixel imaging (SPI) is a promising technique for wide-wavelength, low-light-level measurements. Conventional SPI-based edge-enhanced techniques have used shifting illumination patterns; however, this increases the number of the illumination patterns. We propose two deep neural networks to obtain SPI-based edge images without shifting illumination patterns. The first network is an end-to-end mapping between the measured intensities and entire edge image. The latter comprises two path convolutional layers for restoring horizontal and vertical edges individually; subsequently, both edges are combined to obtain full edge reconstructions, such as in the Sobel filter.

Accelerating hologram generation using oriented-separable convolution and wavefront recording planes.

Recently, holographic displays have gained attention owing to their natural presentation of three-dimensional (3D) images; however, the enormous amount of computation has hindered their applicability. This study proposes an oriented-separable convolution accelerated using the wavefront-recording plane (WRP) method and recurrence formulas. We discuss the orientation of 3D objects that affects computational efficiency, which is overcome by reconsidering the orientation, and the suitability of the proposed method for hardware implementations.

Real-time single-pixel imaging using a system on a chip field-programmable gate array.

Unlike conventional imaging, the single-pixel imaging technique uses a single-element detector, which enables high sensitivity, broad wavelength, and noise robustness imaging. However, it has several challenges, particularly requiring extensive computations for image reconstruction with high image quality. Therefore, high-performance computers are required for real-time reconstruction with higher image quality. In this study, we developed a compact dedicated computer for single-pixel imaging using a system on a chip field-programmable gate array (FPGA), which enables real-time reconstruction at 40 frames per second with an image size of 128 × 128 pixels. An FPGA circuit was implemented with the proposed reconstruction algorithm to obtain higher image quality by introducing encoding mask pattern optimization. The dedicated computer can accelerate the reconstruction 10 times faster than a recent CPU. Because it is very compact compared with typical computers, it can expand the application of single-pixel imaging to the Internet of Things and outdoor applications.

Temporal super-resolution high-speed holographic video recording based on switching reference lights and angular multiplexing in off-axis digital holography.

We develop a temporal super-resolution high-speed holographic video recording method based on the angular multiplexing in off-axis digital holography that can achieve an acquisition rate greater than the frame rate of image sensors. We realize a high-speed switching of reference lights with different incident angles using two acousto-optic modulators. We successfully double the frame rate of the hologram recording using a rotating circular protractor and demonstrate its practical application in compressed gas flow injection; we achieve a frame rate of 175,000 fps using a high-speed image sensor triggered at 87,500 Hz.

Hyperparameter tuning of optical neural network classifiers for high-order Gaussian beams.

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.

Real-valued layer-based hologram calculation.

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.

FFT-based simulation of the hologram-recording process for light-in-flight recording by holography.

We propose a numerical simulation method of the hologram-recording process for light-in-flight recording by holography (LIF holography) based on fast Fourier transform (FFT) to improve the efficiency of the simulation. Because it is crucial to consider the difference in the optical-path length between the object and reference light pulses, we modify a point-spread function by considering the optical-path lengths of the object and reference light pulses and whether both pulses interfere with each other in LIF holography. The computational time was shortened by 5.5×105 times for the 4,096×4,096 resolution of the hologram using the proposed method. We evaluate the proposed method by calculating the root mean square error (RMSE) of the reconstructed holographic images. The RMSEs were relatively small considering the effect of speckle noise; these results effectively demonstrate the validity of the proposed method. Moreover, we reconstruct the moving pictures of light pulse propagation from the hologram generated by the proposed method. We compare the simulation and experimental results, and succeed in qualitatively demonstrating the validity of the proposed method.

Real-valued diffraction calculations for computational holography [Invited].

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.

Acceleration of polygon-based computer-generated holograms using look-up tables and reduction of the table size via principal component analysis.

In this study, we first analyze the fully analytical frequency spectrum solving method based on three-dimensional affine transform. Thus, we establish a new method for combining look-up tables (LUTs) with polygon holography. The proposed method was implemented and proved to be accelerated about twice compared to the existing methods. In addition, principal component analysis was used to compress the LUTs, effectively reducing the required memory without artifacts. Finally, we calculated very complex objects on a graphics processing unit using the proposed method, and the calculation speed was higher than that of existing polygon-based methods.

Spatiotemporal observation of light propagation in a three-dimensional scattering medium.

Spatiotemporal information about light pulse propagation obtained with femtosecond temporal resolution plays an important role in understanding transient phenomena and light-matter interactions. Although ultrafast optical imaging techniques have been developed, it is still difficult to capture light pulse propagation spatiotemporally. Furthermore, imaging through a three-dimensional (3-D) scattering medium is a longstanding challenge due to the optical scattering caused by the interaction between light pulse and a 3-D scattering medium. Here, we propose a technique for ultrafast optical imaging of light pulses propagating inside a 3D scattering medium. We record an image of the light pulse propagation using the ultrashort light pulse even when the interaction between light pulse and a 3-D scattering medium causes the optical scattering. We demonstrated our proposed technique by recording converging, refracted, and diffracted propagating light for 59 ps with femtosecond temporal resolution.

Mitigating ringing artifacts in diffraction calculations using average subtractions.

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.

Hologram computation using the radial point spread function.

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.

GPU-accelerated calculation of computer-generated holograms for line-drawn objects.

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.

An interactive holographic projection system that uses a hand-drawn interface with a consumer CPU.

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.

Single-pixel imaging using a recurrent neural network combined with convolutional layers.

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.

Analytic computation of line-drawn objects in computer generated holography.

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.