arXiv subjects

Yuki Nagahama

Publications and source records attributed to Yuki Nagahama.

11 recordsLinked to original sources

arXiv2025

Introducing GPGPUs to smartphone-based digital holographic microscope for 3D imaging

Digital holography (DH) enables non-contact, noninvasive 3D imaging of transparent and moving microscopic samples by capturing amplitude and phase information in a single shot. In this work, we present a compact, low-cost, real-time smartphone-based DHM system accelerated by GPUs. The system comprises a 3D-printed optical system using readily available image sensors and lasers, coupled with an Android app for hologram reconstruction, extracting amplitude and phase information. Results show a frame rate improvement of approximately 1.65x compared to a CPU-only system. This inexpensive, compact DHM, combining 3D-printed optics and smartphone-based reconstruction, offers a novel approach compared to existing systems and holds promise for fieldwork and remote diagnostics.

physics.optics↗

arXiv2025

Camera Measurement of Blood Oxygen Saturation

Blood oxygen saturation (SpO2) is a crucial vital sign routinely monitored in medical settings. Traditional methods require dedicated contact sensors, limiting accessibility and comfort. This study presents a deep learning framework for contactless SpO2 measurement using an off-the-shelf camera, addressing challenges related to lighting variations and skin tone diversity. We conducted two large-scale studies with diverse participants and evaluated our method against traditional signal processing approaches in intra- and inter-dataset scenarios. Our approach demonstrated consistent accuracy across demographic groups, highlighting the feasibility of camera-based SpO2 monitoring as a scalable and non-invasive tool for remote health assessment.

cs.CE↗

arXiv2024

Interactive zoom display in smartphone-based digital holographic microscope for 3D imaging

Digital holography has applications in bio-imaging because it can simultaneously obtain the amplitude and phase information of a microscopic sample in a single shot, thus facilitating non-contact, noninvasive observation of the 3D shape of transparent objects (phase objects, which can be mapped with the phase information,) and moving objects. The combination of digital holography and microscopy is called digital holographic microscopy (DHM). In this study, we propose a smartphone-based DHM system for 3D imaging that is compact, inexpensive, and capable of observing objects in real time; this system includes an optical system comprising a 3D printer using commercially available image sensors and semiconductor lasers; further, an Android-based application is used to reconstruct the holograms acquired by this optical system, thus outlining the amplitude and phase information of the observed object. Also, by utilizing scalable diffraction calculation methods and touchscreen interaction, we implemented zoom functionality through pinch-in gestures. The study results showed that the DHM system successfully obtained the amplitude and phase information of the observed object via the acquired holograms in an almost real time manner. Thus, this study showed that it is possible to construct a low cost and compact DHM system that includes a 3D printer to construct the optical system and a smartphone application to reconstruct the holograms. Furthermore, this smartphone-based DHM system's ability to capture, reconstruct, and display holograms in real time demonstrates its superiority and novelty over existing systems. This system is also expected to contribute to biology fieldwork and pathological diagnosis in remote areas.

cs.GR↗

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↗

arXiv2017

Fast, large-scale hologram calculation in wavelet domain

We propose a large-scale hologram calculation using WAvelet ShrinkAge-Based superpositIon (WASABI), a wavelet transform-based algorithm. An image-type hologram calculated using the WASABI method is printed on a glass substrate with the resolution of $65,536 \times 65,536$ pixels and a pixel pitch of $1 μ$m. The hologram calculation time amounts to approximately 354 s on a commercial CPU, which is approximately 30 times faster than conventional methods.

cs.GR↗

arXiv2017

Deep-learning-based data page classification for holographic memory

We propose a deep-learning-based classification of data pages used in holographic memory. We numerically investigated the classification performance of a conventional multi-layer perceptron (MLP) and a deep neural network, under the condition that reconstructed page data are contaminated by some noise and are randomly laterally shifted. The MLP was found to have a classification accuracy of 91.58%, whereas the deep neural network was able to classify data pages at an accuracy of 99.98%. The accuracy of the deep neural network is two orders of magnitude better than the MLP.

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↗