arXiv subjects

Yutaka Endo

Publications and source records attributed to Yutaka Endo.

13 recordsLinked to original sources

arXiv2024

Quantized neural network for complex hologram generation

Computer-generated holography (CGH) is a promising technology for augmented reality displays, such as head-mounted or head-up displays. However, its high computational demand makes it impractical for implementation. Recent efforts to integrate neural networks into CGH have successfully accelerated computing speed, demonstrating the potential to overcome the trade-off between computational cost and image quality. Nevertheless, deploying neural network-based CGH algorithms on computationally limited embedded systems requires more efficient models with lower computational cost, memory footprint, and power consumption. In this study, we developed a lightweight model for complex hologram generation by introducing neural network quantization. Specifically, we built a model based on tensor holography and quantized it from 32-bit floating-point precision (FP32) to 8-bit integer precision (INT8). Our performance evaluation shows that the proposed INT8 model achieves hologram quality comparable to that of the FP32 model while reducing the model size by approximately 70% and increasing the speed fourfold. Additionally, we implemented the INT8 model on a system-on-module to demonstrate its deployability on embedded platforms and high power efficiency.

cs.CV↗

arXiv2022

Gigahertz-rate random speckle projection for high-speed single-pixel image classification

Imaging techniques based on single-pixel detection, such as ghost imaging, can reconstruct or recognize a target scene from multiple measurements using a sequence of random mask patterns. However, the processing speed is limited by the low rate of the pattern generation. In this study, we propose an ultrafast method for random speckle pattern generation, which has the potential to overcome the limited processing speed. The proposed approach is based on multimode fiber speckles induced by fast optical phase modulation. We experimentally demonstrate dynamic speckle projection with phase modulation at 10 GHz rates, which is five to six orders of magnitude higher than conventional modulation approaches using spatial light modulators. Moreover, we combine the proposed generation approach with a wavelength-division multiplexing technique and apply it for image classification. As a proof-of-concept demonstration, we show that 28x28-pixel images of digits acquired at GHz rates can be accurately classified using a simple neural network. The proposed approach opens a novel pathway for an all-optical image processor.

physics.optics↗

arXiv2018

Digital holographic particle volume reconstruction using a deep neural network

This paper proposes a particle volume reconstruction directly from an in-line hologram using a deep neural network. Digital holographic volume reconstruction conventionally uses multiple diffraction calculations to obtain sectional reconstructed images from an in-line hologram, followed by detection of the lateral and axial positions, and the sizes of particles by using focus metrics. However, the axial resolution is limited by the numerical aperture of the optical system, and the processes are time-consuming. The method proposed here can simultaneously detect the lateral and axial positions, and the particle sizes via a deep neural network (DNN). We numerically investigated the performance of the DNN in terms of the errors in the detected positions and sizes. The calculation time is faster than conventional diffracted-based approaches.

eess.IV↗

arXiv2017

Computational ghost imaging using deep learning

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.

cs.CV↗

arXiv2016

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 QR codes by using the proposed method.

cs.CV↗

arXiv2015

Improvement of the image quality of random phase--free holography using an iterative method

Our proposed method of random phase-free holography using virtual convergence light can obtain large reconstructed images exceeding the size of the hologram, without the assistance of random phase. The reconstructed images have low-speckle noise in the amplitude and phase-only holograms (kinoforms); however, in low-resolution holograms, we obtain a degraded image quality compared to the original image. We propose an iterative random phase-free method with virtual convergence light to address this problem.

physics.optics↗

arXiv2015

Optical encryption for large-sized images using random phase-free method

We propose an optical encryption framework that can encrypt and decrypt large-sized images beyond the size of the encrypted image using our two methods: random phase-free method and scaled diffraction. In order to record the entire image information on the encrypted image, the large-sized images require the random phase to widely diffuse the object light over the encrypted image; however, the random phase gives rise to the speckle noise on the decrypted images, and it may be difficult to recognize the decrypted images. In order to reduce the speckle noise, we apply our random phase-free method to the framework. In addition, we employ scaled diffraction that calculates light propagation between planes with different sizes by changing the sampling rates.

physics.optics↗

arXiv2015

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.

physics.optics↗

arXiv2014

Numerical investigation of lensless zoomable holographic multiple projections to tilted planes

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.

physics.optics↗

arXiv2014

Ptychography by changing the area of probe light and scaled ptychography

Ptychography is a promising phase retrieval technique for visible light, X-ray and electron beams. Conventional ptychography reconstructs the amplitude and phase of an object light from a set of the diffraction intensity patterns obtained by the X-Y moving of the probe light. The X-Y moving of the probe light requires two control parameters and accuracy of the locations. We propose ptychography by changing the area of the probe light using only one control parameter, instead of the X-Y moving of the probe light. The proposed method has faster convergence speed. In addition, we propose scaled ptychography using scaled diffraction calculation in order to magnify retrieved object lights clearly.

physics.optics↗

arXiv2013

Non-uniform sampled scalar diffraction calculation using non-uniform fast Fourier transform

Scalar diffraction calculations such as the angular spectrum method (ASM) and Fresnel diffraction, are widely used in the research fields of optics, X-rays, electron beams, and ultrasonics. It is possible to accelerate the calculation using fast Fourier transform (FFT); unfortunately, acceleration of the calculation of non-uniform sampled planes is limited due to the property of the FFT that imposes uniform sampling. In addition, it gives rise to wasteful sampling data if we calculate a plane having locally low and high spatial frequencies. In this paper, we developed non-uniform sampled ASM and Fresnel diffraction to improve the problem using the non-uniform FFT.

physics.optics↗

arXiv2013

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

We report a calculation reduction method for color computer-generated holograms (CGHs) using color space conversion. Color CGHs are generally calculated on RGB space. In this paper, we calculate color CGHs in other color spaces: for example, YCbCr color space. In YCbCr color space, a RGB image 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 CGHs. We compute diffraction calculations from the components, and then we convert the diffracted results in YCbCr color space to RGB color space.

physics.optics↗

arXiv2013

Gigapixel inline digital holographic microscopy using a consumer scanner

We demonstrate a gigapixel inline digital holographic microscopy using a consumer scanner. The consumer scanner can maximally scan an A4 size image (297mm x 210mm) with 4800 dpi (= 5.29 um), theoretically achieving a resolution of 56,144 x 39,698 = 2.22 gigapixels. The system using a consumer scanner has a simple structure, compared with synthetic aperture digital holography using a camera mounted on a two-dimensional moving stage. In this demonstration, we captured an inline hologram with 23,602 x 18,023 pixels (= 0.43 gigapixels). In addition, to accelerate the reconstruction time of the gigapixel hologram and decrease the amount of memory for the reconstruction, we applied the band-limited double-step Fresnel diffraction to the reconstruction.

physics.optics↗