Quantification of skin quality through speckle analysis

Lotay, Amrit; Buttenschoen, Kim-Kristin K.; Girkin, John M.

doi:10.1117/12.2077616

Cited by 4 publications

(1 citation statement)

References 5 publications

(3 reference statements)

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“…The opposite attitude of exploiting laser speckle to non-invasively characterize surfaces has also been widely pursued 12,[18][19][20][21][22][23][24] . The prevalent method involves reconstructing the amplitude and phase of the optical field, which is notoriously difficult 2,25,26 due to the multitude of vortices in the speckle pattern.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive Estimation of the Powder Size Distribution from a Single Speckle Image

Zhang,

Barbastathis,

Pandit

et al. 2024

Preprint

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Non-invasive characterization of powders may take one of two approaches: imaging and counting individual particles; or relying on scattered light to estimate the particle size distribution (PSD) of the ensemble. The former approach runs into practical difficulties, as the system must conform to the working distance and other restrictions of the imaging optics. The latter approach requires an inverse map from the speckle autocorrelation to the particle sizes. The principle relies on the pupil function determining the basic sidelobe shape, whereas the particle size spread modulates the sidelobe intensity. We recently showed that it is feasible to invert the speckle autocorrelation and obtain the PSD using a neural network, trained efficiently through a physics-informed semi-generative approach. In this work, we eliminate one of the most time-consuming steps of our previous method by engineering the pupil function. By judiciously blocking portions of the pupil, we sacrifice some photons but in return we achieve much taller enhanced sidelobes and, hence, higher sensitivity to the change of the size distribution. The result is a 60× reduction in total acquisition and processing time, or 0.25 seconds per frame in our implementation. Almost real-time operation in our system is not only more appealing toward rapid industrial adoption, it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying, blending, and other chemical and pharmaceutical manufacturing processes.

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Section: Introductionmentioning

confidence: 99%

Non-invasive Estimation of the Powder Size Distribution from a Single Speckle Image

Zhang,

Barbastathis,

Pandit

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Non-invasive estimation of the powder size distribution from a single speckle image

Zhang,

Pandit,

Liu

et al. 2024

Light Sci Appl

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Non-invasive characterization of powders may take one of two approaches: imaging and counting individual particles; or relying on scattered light to estimate the particle size distribution (PSD) of the ensemble. The former approach runs into practical difficulties, as the system must conform to the working distance and other restrictions of the imaging optics. The latter approach requires an inverse map from the speckle autocorrelation to the particle sizes. The principle relies on the pupil function determining the basic sidelobe shape, whereas the particle size spread modulates the sidelobe intensity. We recently showed that it is feasible to invert the speckle autocorrelation and obtain the PSD using a neural network, trained efficiently through a physics-informed semi-generative approach. In this work, we eliminate one of the most time-consuming steps of our previous method by engineering the pupil function. By judiciously blocking portions of the pupil, we sacrifice some photons but in return we achieve much enhanced sidelobes and, hence, higher sensitivity to the change of the size distribution. The result is a 60 × reduction in total acquisition and processing time, or 0.25 seconds per frame in our implementation. Almost real-time operation in our system is not only more appealing toward rapid industrial adoption, it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying, blending, and other chemical and pharmaceutical manufacturing processes.

show abstract

Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)

et al. 2023

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Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.

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Quantification of skin quality through speckle analysis

Cited by 4 publications

References 5 publications

Non-invasive Estimation of the Powder Size Distribution from a Single Speckle Image

Non-invasive Estimation of the Powder Size Distribution from a Single Speckle Image

Non-invasive estimation of the powder size distribution from a single speckle image

Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)

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