Autofocus Method for Digital Holographic Reconstruction of Microscopic Object

Wang, Huaying; Qin, Aili; Huang, Min

doi:10.1109/sopo.2009.5230170

Cited by 9 publications

(7 citation statements)

References 11 publications

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“…In order to determine the sharpest image, a digital autofocussing algorithm is used. Of the various autofocus evaluation functions, like the variance of grey level function, the weighted Fourier spectral function and the standard deviation correlation function, the variance of grey level is the most sensitive to detect sharp images (Wang et al, 2009). It is hence chosen to determine the depth of the object.…”

Section: Digital Reconstructionmentioning

confidence: 99%

Simultaneous in situ characterisation of bubble dynamics and a spatially resolved concentration profile: a combined Mach–Zehnder holography and confocal Raman-spectroscopy sensor system

Guhathakurta

Schurr

Rinke

et al. 2017

J. Sens. Sens. Syst.

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Abstract. For a reaction between a gaseous phase and a liquid phase, the interaction between the hydrodynamic conditions, mass transport and reaction kinetics plays a crucial role with respect to the conversion and selectivity of the process. Within this work, a sensor system was developed to simultaneously characterise the bubble dynamics and the localised concentration measurement around the bubbles. The sensor system is a combination of a digital Mach-Zehnder holography subsystem to measure bubble dynamics and a confocal Raman-spectroscopy subsystem to measure localised concentration. The combined system was used to investigate the chemical absorption of CO 2 bubbles in caustic soda in microchannels. The proposed set-up is explained and characterised in detail and the experimental results are presented, illustrating the capability of the sensor system to simultaneously measure the localised concentration of the carbonate ion with a good limit of detection and the 3-D position of the bubble with respect to the spot where the concentration was measured.

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Section: Digital Reconstructionmentioning

confidence: 99%

Simultaneous in situ characterisation of bubble dynamics and a spatially resolved concentration profile: a combined Mach–Zehnder holography and confocal Raman-spectroscopy sensor system

Guhathakurta

Schurr

Rinke

et al. 2017

J. Sens. Sens. Syst.

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show abstract

“…In order to have an automatic measure of the sharpest image digital autofocussing algorithm is used. Out of the various auto-focus evaluation functions like variance of grey function, weighted Fourier spectral function and standard deviation correlation function etc., the variance of grey level shows the most sensitivity to detecting sharp images [13] and is hence chosen for determining the depth of the object. The measure of image sharpness by this method is given by:…”

Section: The Reconstruction Distancementioning

confidence: 99%

6.1.4 - Accuracy of 3D position measurement of spherical objects with a holographic single camera setup

Guhathakurta¹,

Li²,

Simon³

2016

Tagungsband

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“…1(b)). Several automated approaches have been proposed to determine the best focus plane for amplitude-contrast or phase-contrast images in related applications [25][26][27][28][29][30]. Generally, multiple images at different focus planes are numerically reconstructed, and a function evaluates the quality of each image.…”

Section: Introductionmentioning

confidence: 99%

No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network

Jaferzadeh¹,

Hwang²,

Moon³

et al. 2019

Biomed. Opt. Express

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Digital propagation of an off-axis hologram can provide the quantitative phase-contrast image if the exact distance between the sensor plane (such as CCD) and the reconstruction plane is correctly provided. In this paper, we present a deep-learning convolutional neural network with a regression layer as the top layer to estimate the best reconstruction distance. The experimental results obtained using microsphere beads and red blood cells show that the proposed method can accurately predict the propagation distance from a filtered hologram. The result is compared with the conventional automatic focus-evaluation function. Additionally, our approach can be utilized at the single-cell level, which is useful for cell-to-cell depth measurement and cell adherent studies.

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Autofocus Method for Digital Holographic Reconstruction of Microscopic Object

Cited by 9 publications

References 11 publications

Simultaneous in situ characterisation of bubble dynamics and a spatially resolved concentration profile: a combined Mach–Zehnder holography and confocal Raman-spectroscopy sensor system

Simultaneous in situ characterisation of bubble dynamics and a spatially resolved concentration profile: a combined Mach–Zehnder holography and confocal Raman-spectroscopy sensor system

6.1.4 - Accuracy of 3D position measurement of spherical objects with a holographic single camera setup

No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network

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