Single-shot and lensless complex-amplitude imaging with incoherent light based on machine learning

Horisaki, Ryoichi; Fujii, Kazuki; Tanida, Jun

doi:10.1007/s10043-018-0452-1

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…This could be realized with machine learning that has been suggested, e.g., to automize data handling in holographic imaging of MPs [25]. Yet, machine learning is efficient also in such a case to monitor, e.g., 3D structures of transparent microscopic objects [26] but would require some changes in the case of the detection setup to obtain interesting 3D appearance of transparent MPs. Note that the method described by Horisaki et al can be used also for opaque objects by detecting reflected light from an object.…”

Section: Methodsmentioning

confidence: 99%

Sorting microplastics from other materials in water samples by ultra-high-definition imaging

Roussey¹

2023

J. Eur. Opt. Society-Rapid Publ.

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In this study a commercial particle analyzer was used to image and help sorting microplastic particles (MPs) dispersed in filtrated and de-aerated tap water. The device provides a relatively easy and fast procedure for obtaining ultra-high-definition imaging, allowing the determination of shape, size, and number of 2D-projections of solid particles. The image analysis revealed clear differences among the studied different MPs originating from the grinding of five common grades of plastic sheets as they affect the image rendering differently, principally due to the light scattering either at the surface or in the volume of the microplastics. The high-quality imaging of the device also allows the discrimination of the microplastics from air bubbles with well-defined spherical shapes as well as to obtain an estimate of the size of MPs in a snapshot. We associate the differences among the shapes of the identified MPs in this study depending on the plastic type with known physical properties, such as brittleness, crystallinity, or softness. Furthermore, as a novel method we exploit a parameter based on the light intensity map from moving particles in cuvette flow to sort MPs from other particles, such as, wood fiber, human hair, and air bubbles. Using the light intensity map, which is related to the plastic-water refractive index ratio, the presence of microplastics in water can be revealed among other particles, but not their specific plastic type.

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Section: Methodsmentioning

confidence: 99%

Sorting microplastics from other materials in water samples by ultra-high-definition imaging

Roussey¹

2023

J. Eur. Opt. Society-Rapid Publ.

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“…Wang Pan used synthetic aperture imaging technology to improve the resolution of incoherent digital holography [12]. Teruyoshi and Horisaki's group at Jinan University used light-emitting diodes (LEDs) as the illumination source and studied the effect of light source size and diffraction distance on the resolution of hologram reproduction [13] and optimized the quality of phase reconstruction [14]. Teruyoshi et al from Huazhong University of Science and Technology improved the quality of incoherent light imaging by investigating the imaging system's signal-tonoise ratio and edge contrast [15].…”

Section: Noncoherent Optical Digital Holographic Imagingmentioning

confidence: 99%

A Study of Resolution Improvement in Noncoherent Optical Coherence Imaging

Yang

et al. 2022

Advances in Mathematical Physics

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Noncoherent light, as a common light source in life, can effectively avoid problems such as scattering noise caused by optical components incoherent light imaging, and through the design of the optical path can also trigger interference and holographic imaging of objects, allowing holography to be used in more fields. Various techniques have emerged for recording holograms using incoherent light sources as technology has developed. A recording method has been proposed that exploits the correlation between the object wave information and the Fresnel band sheet to achieve incoherent hologram recording. Using a spatial light modulator (SLM) loaded with a bit-phase mask with multiplexed lens function, the incident light wavefield is phase-modulated to achieve diffraction spectroscopy and phase shifting. And holograms with different phase shifts can be obtained and combined with phase-shifting techniques to eliminate the effects of twin images caused by coaxial holography in the reproduction process. Based on the study of this incoherent holographic imaging system, the influence of the characteristics of the main components of the system and the corresponding parameters on the resolution of the recorded and reproduced holograms is investigated, and optimization methods are given from both theoretical and experimental studies. The empirical analysis of the FINCH imaging system is carried out. The observed optical path is designed, and the method of making a bit-phase mask loaded on a spatial light modulator is presented. The effect of the focal length and recording distance of the dislocation mask on the resolution of the system is investigated by both computer simulation and experimental operation.

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“…Moreover, the multi-capture operation also decreases the data transfer rate. Lin proposed non-interferometric phase demodulation based on an iterative Fourier transform algorithm 12,19 . The optical system is simplified, but the data transfer rate decreases because of the iterative calculation in the retrieval process.…”

Section: Introductionmentioning

confidence: 99%

Lensless complex amplitude demodulation based on deep learning in holographic data storage

Hao¹,

Lin²,

Lin³

et al. 2023

OEA

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To increase the storage capacity in holographic data storage (HDS), the information to be stored is encoded into a complex amplitude. Fast and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout in HDS. In this study, we proposed a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level phase. By analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages, the inverse problem was decomposed into two backward operators denoted by two convolutional neural networks (CNNs) to demodulate amplitude and phase respectively. The experimental system is simple, stable, and robust, and it only needs a single diffraction image to realize the direct demodulation of both amplitude and phase. To our investigation, this is the first time in HDS that multilevel complex amplitude demodulation is achieved experimentally from one diffraction intensity image without iterations.

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Single-shot and lensless complex-amplitude imaging with incoherent light based on machine learning

Cited by 8 publications

References 34 publications

Sorting microplastics from other materials in water samples by ultra-high-definition imaging

Sorting microplastics from other materials in water samples by ultra-high-definition imaging

A Study of Resolution Improvement in Noncoherent Optical Coherence Imaging

Lensless complex amplitude demodulation based on deep learning in holographic data storage

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