Satoki Hasegawa

Random phase-free kinoform for large objects.

We propose a random phase-free kinoform for large objects. When not using the random phase in kinoform calculation, the reconstructed images from the kinoform are heavy degraded, like edge-only preserved images. In addition, the kinoform cannot record an entire object that exceeds the kinoform size because the object light does not widely spread. In order to avoid this degradation and to widely spread the object light, the random phase is applied to the kinoform calculation; however, the reconstructed image is contaminated by speckle noise. In this paper, we overcome this problem by using our random phase-free method and error diffusion method.

Numerical investigation of lensless zoomable holographic multiple projections to tilted planes

Abstract This paper numerically investigates the feasibility of lensless zoomable holographic multiple projections to tilted planes. We have already developed lensless zoomable holographic single projection using scaled diffraction, which calculates diffraction between parallel planes with different sampling pitches. The structure of this zoomable holographic projection is very simple because it does not need a lens; however, it only projects a single image to a plane parallel to the hologram. The lensless zoomable holographic projection in this paper is capable of projecting multiple images onto tilted planes simultaneously.

Computational ghost imaging using deep learning

Abstract Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two- or three-dimensional images with a single or a few bucket detectors, the quality of the reconstructed images is reduced by noise due to the reconstruction of images from random patterns. In this study, we improve the quality of CGI images using deep learning. A deep neural network is used to automatically learn the features of noise-contaminated CGI images. After training, the network is able to predict low-noise images from new noise-contaminated CGI images.

Holographic microinformation hiding

We propose a holographic microinformation hiding scheme in which the embedding information to be embedded is small and imperceptible to the human eyes. This scheme converts the embedding information into a complex amplitude via scaled diffraction. The complex amplitude of the reduced embedding information is added to the complex amplitude of the host image, followed by conversion to a hologram. The reduced embedded information is inconspicuous from the hologram during the reconstruction process; however, the reduction leads to the degradation of the embedded image quality. Therefore, to improve the quality of the embedded image quality, we employ iterative optimization and the time averaging effect of multiple holograms.

Color computer-generated hologram generation using the random phase-free method and color space conversion

We propose two calculation methods of generating color computer-generated holograms (CGHs) with the random phase-free method and color space conversion in order to improve the image quality and accelerate the calculation. The random phase-free method improves the image quality in monochrome CGH, but it is not performed in color CGH. We first aimed to improve the image quality of color CGH using the random phase-free method and then to accelerate the color CGH generation with a combination of the random phase-free method and color space conversion method, which accelerates the color CGH calculation due to down-sampling of the color components converted by color space conversion. To overcome the problem of image quality degradation that occurs due to the down-sampling of random phases, the combination of the random phase-free method and color space conversion method improves the quality of reconstructed images and accelerates the color CGH calculation. We demonstrated the effectiveness of the proposed method in simulation, and in this paper discuss its application to lensless zoomable holographic projection.

A computational approach to real-time image processing for serial time-encoded amplified microscopy

High-speed imaging is an indispensable technique, particularly for identifying or analyzing fast-moving objects. The serial time-encoded amplified microscopy (STEAM) technique was proposed to enable us to capture images with a frame rate 1,000 times faster than using conventional methods such as CCD (charge-coupled device) cameras. The application of this high-speed STEAM imaging technique to a real-time system, such as flow cytometry for a cell-sorting system, requires successively processing a large number of captured images with high throughput in real time. We are now developing a high-speed flow cytometer system including a STEAM camera. In this paper, we describe our approach to processing these large amounts of image data in real time. We use an analog-to-digital converter that has up to 7.0G samples/s and 8-bit resolution for capturing the output voltage signal that involves grayscale images from the STEAM camera. Therefore the direct data output from the STEAM camera generates 7.0G byte/s continuously. We provided a field-programmable gate array (FPGA) device as a digital signal pre-processor for image reconstruction and finding objects in a microfluidic channel with high data rates in real time. We also utilized graphics processing unit (GPU) devices for accelerating the calculation speed of identification of the reconstructed images. We built our prototype system, which including a STEAM camera, a FPGA device and a GPU device, and evaluated its performance in real-time identification of small particles (beads), as virtual biological cells, owing through a microfluidic channel.