Yuki Nagahama

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.

Autoencoder-based holographic image restoration

We propose a holographic image restoration method using an autoencoder, which is an artificial neural network. Because holographic reconstructed images are often contaminated by direct light, conjugate light, and speckle noise, the discrimination of reconstructed images may be difficult. In this paper, we demonstrate the restoration of reconstructed images from holograms that record page data in holographic memory and quick response codes by using the proposed method.

Calculation reduction method for color digital holography and computer-generated hologram using color space conversion

Abstract. A calculation reduction method for color digital holography (DH) and computer-generated holograms (CGHs) using color space conversion is reported. Color DH and color CGHs are generally calculated on RGB space. We calculate color DH and CGHs in other color spaces for accelerating the calculation (e.g., YCbCr color space). In YCbCr color space, a RGB image or RGB hologram is converted to the luminance component (Y), blue-difference chroma (Cb), and red-difference chroma (Cr) components. In terms of the human eye, although the negligible difference of the luminance component is well recognized, the difference of the other components is not. In this method, the luminance component is normal sampled and the chroma components are down-sampled. The down-sampling allows us to accelerate the calculation of the color DH and CGHs. We compute diffraction calculations from the components, and then we convert the diffracted results in YCbCr color space to RGB color space. The proposed method, which is possible to accelerate the calculations up to a factor of 3 in theory, accelerates the calculation over two times faster than the ones in RGB color space.

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.

Holographic multi-projection using the random phase-free method.

We demonstrated holographic multi-projection using the random phase-free method and the iterative method. Holographic multi-projection is a method of projecting multiple different images focused on different screens at the same time. The random phase-free method succeeded in improving the image quality. By applying the iterative method to the random phase-free method, the image quality was improved further. The results of our numerical reconstruction and optical experiments confirm that the proposed method improves the image quality. The peak signal-to-noise ratios of the reconstructed images using the proposed method and the conventional method are 30.66 and 13.61 dB, respectively.

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.

Speeding up image quality improvement in random phase-free holograms using ringing artifact characteristics

A holographic projector utilizes holography techniques. However, there are several barriers to realizing holographic projections. One is deterioration of hologram image quality caused by speckle noise and ringing artifacts. The combination of the random phase-free method and the Gerchberg-Saxton (GS) algorithm has improved the image quality of holograms. However, the GS algorithm requires significant computation time. We propose faster methods for image quality improvement of random phase-free holograms using the characteristics of ringing artifacts.

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.