Accurate size measurement of needle-shaped particles using digital holography

Khanam, Taslima; Rahman, Mohammad Nurur; Rajendran, Arvind; Kariwala, Vinay; Asundi, Anand

doi:10.1016/j.ces.2011.03.026

Cited by 41 publications

(36 citation statements)

References 21 publications

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“…By minimizing the mean intensity of the particle, the depth of a particle can be determined as [15] z d = arg min…”

Section: Minimum Intensity (Mini) Methodsmentioning

confidence: 99%

“…Digital in-line holography (DIH) has been extensively applied to the detection and characterization of particle fields, where the particles can be tracer particles in flow measurements [1][2][3][4], droplets in spray diagnostics [5][6][7][8], micro-organisms in biological mobility studies [9,10], bubbles in multiphase flows [11,12] and other particles of interest [13][14][15]. Besides the simplicity of the in-line setup, the accessibility to three-dimensional (3D) information has promoted the application of DIH, in which the particle size, shape and 3D location can be measured.…”

Section: Introductionmentioning

confidence: 99%

“…In the reconstruction of particle holograms without filtering of the DC term, the focused image of a particle appears as a dark region with sharp edges in contrast to the bright background. For this reason, the minimum intensity of a reconstructed particle image is commonly used as a focus criterion [15][16][17]. Specifically, the particle depth is determined by locating the z-position where the particle image intensity reaches a minimum.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty characterization of particle depth measurement using digital in-line holography and the hybrid method

Gao¹,

Guildenbecher²,

Reu³

et al. 2013

Opt. Express

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In the detection of particles using digital in-line holography, measurement accuracy is substantially influenced by the hologram processing method. In particular, a number of methods have been proposed to determine the out-of-plane particle depth (z location). However, due to the lack of consistent uncertainty characterization, it has been unclear which method is best suited to a given measurement problem. In this work, depth determination accuracies of seven particle detection methods, including a recently proposed hybrid method, are systematically investigated in terms of relative depth measurement errors and uncertainties. Both synthetic and experimental holograms of particle fields are considered at conditions relevant to particle sizing and tracking. While all methods display a range of particle conditions where they are most accurate, in general the hybrid method is shown to be the most robust with depth uncertainty less than twice the particle diameter over a wide range of particle field conditions.

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“…By minimizing the mean intensity of the particle, the depth of a particle can be determined as [15] z d = arg min…”

Section: Minimum Intensity (Mini) Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty characterization of particle depth measurement using digital in-line holography and the hybrid method

Gao¹,

Guildenbecher²,

Reu³

et al. 2013

Opt. Express

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show abstract

“…For these applications, the most widely used methods now are particle image velocimetry (PIV), 21-23 laser speckle velocimetry (LSV), 24 and electronic 25 or digital 26,27 speckle photography. 29,30 As previously noted, the stages of recording of the particle images and their postprocessing are frequently combined and performed successively within one work. 28 There are various relevant situations, e.g., when the test image consists of a mixture of moving and stationary parts.…”

Section: Postprocessing and Analysis Of Particle Imagesmentioning

confidence: 99%

Characterization of particles suspended in a volume of optical medium at high concentrations by coherent image processing

Nikolaeva

Petrov

2015

Opt. Eng

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Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/17/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 083101-2 August 2015 • Vol. 54(8) Nikolaeva and Petrov: Characterization of particles suspended in a volume. . . Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/17/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 083101-4 August 2015 • Vol. 54(8) Nikolaeva and Petrov: Characterization of particles suspended in a volume. . . Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/17/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 083101-6 August 2015 • Vol. 54(8) Nikolaeva and Petrov: Characterization of particles suspended in a volume. . . Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/17/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 083101-7 August 2015 • Vol. 54(8) Nikolaeva and Petrov: Characterization of particles suspended in a volume. . . Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/17/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 083101-8 August 2015 • Vol. 54(8) Nikolaeva and Petrov: Characterization of particles suspended in a volume. . . Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/17/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Optical Engineering 083101-9 August 2015 • Vol. 54(8) Nikolaeva and Petrov: Characterization of particles suspended in a volume. . . Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/17/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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“…Using this approach, Talapatra et al conducted a measurement of bubble size distribution in a ship wake using holograms captured by a DIH setup towed by the ship and Li et al (2017) measured the size distribution of droplets generated by a breaking wave impinging on an oil slick. This method must assume that particles have a spherical shape which largely limits its application in measurement tasks, especially for solid particles with irregular shapes (Khanam et al 2011 andWu et al 2014). Recent development by Shao et al (2019a) has combined a minimum intensity focus metric and wavelet-based focus metric to achieve a 3D distribution of particles with a wide range of sizes.…”

Section: Introductionmentioning

confidence: 99%

Machine learning holography for 3D particle field imaging

Shao¹,

Mallery²,

Kumar³

et al. 2020

Opt. Express

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Particle size measurement based on digital holography with conventional algorithms are usually time-consuming and susceptible to noises associated with hologram quality and particle complexity, limiting its usage in a broad range of engineering applications and fundamental research. We propose a learning-based hologram processing method to cope with the aforementioned issues. The proposed approach uses a modified U-net architecture with three input channels and two output channels, and specially-designed loss functions. The proposed method has been assessed using synthetic, manually-labeled experimental, and water tunnel bubbly flow data containing particles of different shapes. The results demonstrate that our approach can achieve better performance in comparison to the state-of-the-art non-machine-learning methods in terms of particle extraction rate and positioning accuracy with significantly improved processing speed. Our learning-based approach can be extended to other types of image-based particle size measurements.

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Accurate size measurement of needle-shaped particles using digital holography

Cited by 41 publications

References 21 publications

Uncertainty characterization of particle depth measurement using digital in-line holography and the hybrid method

Uncertainty characterization of particle depth measurement using digital in-line holography and the hybrid method

Characterization of particles suspended in a volume of optical medium at high concentrations by coherent image processing

Machine learning holography for 3D particle field imaging

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