Computational ghost imaging is a promising technique for single-pixel imaging because it is robust to disturbance and can be operated over broad wavelength bands, unlike common cameras. However, one disadvantage of this method is that it has a long calculation time for image reconstruction. In this paper, we have designed a dedicated calculation circuit that accelerated the process of computational ghost imaging. We implemented this circuit by using a field-programmable gate array, which reduced the calculation time for the circuit compared to a CPU. The dedicated circuit reconstructs images at a frame rate of 300 Hz.

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.

Ghost imaging reconstructs images using a single-element photodetector; it performs imaging by illuminating an object with binary modulation patterns. This technique has various advantages, including a wide wavelength, noise robustness, and high measurement sensitivity. However, one challenge is the low image quality in the undersampling. The examination of modulation patterns is intended to solve this issue. In ghost imaging, randomly generated or basis patterns have been studied as modulation patterns; however, in undersampling, random patterns exhibit noise robustness but low image quality, whereas basis patterns exhibit high image quality but are sensitive to noise and low resolution. Thus, ghost imaging requires patterns that simultaneously achieve high image quality, resolution, and noise robustness. This study proposes a method of pattern optimization using gradient descent and a binarization method to further improve image quality. Numerical simulation and experimental results show that the proposed approach offers high image quality, high resolution, and robustness to noise.

A complex amplitude hologram can reconstruct perfect light waves. However, as there are no spatial light modulators that are able to display complex amplitudes, we need to use amplitude, binary, or phase-only holograms. The images reconstructed from such holograms will deteriorate; to address this problem, iterative hologram optimization algorithms have been proposed. One of the iterative algorithms utilizes a blank area to help converge the optimization; however, the calculation time and memory usage involved increases. In this study, we propose to reduce the computational complexity and memory usage of the iterative optimization using scaled diffraction, which can calculate light propagation with different sampling pitches on a hologram plane and object plane. Scaled diffraction can introduce a virtual blank area without using physical memory. We further propose a combination of scaled diffraction-based optimization and conventional methods. The combination algorithm improves the quality of a reconstructed complex amplitude while accelerating optimization.

Generating new motion parallax holograms is required for holographic head-mounted displays when the head moves. Additionally, it is required for hologram generation from light field data that consist of a number of motion parallax images. However, re-rendering three-dimensional (3D) scenes and re-calculating holograms are computationally complex. Therefore, we propose a generation strategy of holograms with different motion parallax from an existing hologram without re-rendering 3D scenes and re-calculating holograms. The proposed method employs Fourier band-pass filtering and the simple relation of trigonometric functions, which makes it capable of skipping the computationally complex processes.

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.