Phase retrieval method for digital holography with two cameras in particle measurement

Tanaka, Yohsuke; Tani, Shunsuke; Murata, Shigeru

doi:10.1364/oe.24.025233

Cited by 25 publications

(6 citation statements)

References 16 publications

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“…This iterative phase retrieval algorithm is based on the Gerchberg-Saxton algorithm (Gerchberg and Saxton, 1972) using two holograms as intensity constraint conditions. The distance Δz between the two holograms with good convergence of the iterative process is given by (Tanaka et al, 2016)  …”

Section: Suppression Of Twin Image Problem Using Phase Retrieval Holo...mentioning

confidence: 99%

“…: where ϕ 1 and ϕ 2 are the retrieved phases at the first and second hologram, respectively. The iterative loop exits when the mode of the reconstructed image at z p3 becomes the maximum value (Tanaka et al, 2016). The virtual particle image has been removed by phase retrieval holography, as shown in the intensity line profile of Fig.…”

Section: Suppression Of Twin Image Problem Using Phase Retrieval Holo...mentioning

confidence: 99%

“…To solve this problem, phase retrieval holography using two or more cameras has been proposed (Liu and Scott, 1987;Zhang et al, 2003). The present authors have used this method to measure microparticles (Tanaka et al, 2016), microbubbles (Kubonishi et al, 2018), and microdroplets (Nakatani et al, 2019;Tanaka et al, 2019b). However, practical usage of Gabor holography and phase retrieval holography is limited by the huge execution time for volumetric reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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Particle Size Measurement Using a Phase Retrieval Holography System with a GPU-Equipped SBC

Tanaka

Nakai

2024

KONA

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We have developed a phase retrieval holography system using a single-board computer (SBC) with a graphics processing unit (GPU) for particle size measurement. The system comprises two cameras connected to the SBC with a GPU (Jetson Nano TM , NVIDIA ® ), a diode-pumped solid-state green laser, and a beam splitter. The GPU enables us to reconstruct holograms in real-time and measure particle size. The system can record the shapes and positions of particles falling in a static flow in a three-dimensional volume as two holograms generating an interference pattern. Two holograms solve the twin image problem that arises because of the lack of phase information using phase retrieval holography. We also present the requirement of this system for experimentally recording and numerically reconstructing holograms of particles. Finally, we compare the particle size distribution obtained by the system to that of conventional two-dimensional image measurement.

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Section: Suppression Of Twin Image Problem Using Phase Retrieval Holo...mentioning

confidence: 99%

Section: Suppression Of Twin Image Problem Using Phase Retrieval Holo...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Particle Size Measurement Using a Phase Retrieval Holography System with a GPU-Equipped SBC

Tanaka

Nakai

2024

KONA

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“…High-speed 3D imaging can be realized by combining it with high-speed imaging. [14][15][16][17][18][19][20][21][22] It is noteworthy that a faster camera produces a smaller number of pixels of the image sensor. When calculating reconstructed images, computational load is reduced using FFT.…”

Section: Introductionmentioning

confidence: 99%

Special-purpose computer for digital holographic high-speed three-dimensional imaging

et al. 2020

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Digital holography is attracting attention because it can record instantaneous threedimensional (3D) information and record dynamic phenomena. However, when recording highspeed phenomena, the frame rate ranges from tens of thousands to hundreds of millions, and the calculation time of the reconstructed images is a problem. We have developed a special-purpose computer for high-speed 3D imaging using digital holography. The developed special-purpose computer has four calculation modules and has achieved a calculation time 68 times faster than that of a personal computer with 48 cores. With the developed computer, a total of 32 reconstructed images can be calculated in 0.69 ms from four holograms of 128 × 128 pixels with eight varying depths. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Phase information improves the precision of measuring the axial position of particles [4,5]. The volumetric reconstruction of particles using deconvolution and a phase retrieval algorithm has been proposed [6,7].…”

Section: Introductionmentioning

confidence: 99%

Digital holographic particle volume reconstruction using a deep neural network

et al. 2019

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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. *

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Phase retrieval method for digital holography with two cameras in particle measurement

Cited by 25 publications

References 16 publications

Particle Size Measurement Using a Phase Retrieval Holography System with a GPU-Equipped SBC

Particle Size Measurement Using a Phase Retrieval Holography System with a GPU-Equipped SBC

Special-purpose computer for digital holographic high-speed three-dimensional imaging

Digital holographic particle volume reconstruction using a deep neural network

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