Particle tracking and identification using on-chip holographic imaging with inertial separation

Particle shape is a significant feature of irregular particles. The interferometric particle imaging (IPI) technique has been introduced to retrieve submillimetric irregular rough particle shapes, while inevitable experimental noises hinder the convergence of two-dimensional (2D) particle shapes from single speckle patterns. In this work, a hybrid input–output algorithm with shrink-wrap support and oversampling smoothness constraints is utilized to suppress the Poisson noise in IPI measurement and recover accurate 2D shapes of particles. Our method is tested in numerical simulations on ice crystal shapes and actual IPI measurements on four different types of irregular, rough particles. The shape similarity of the reconstructed 2D shape has reached an average Jaccard Index score of 0.927, and the relative deviation of the reconstructed size is within 7% for all 60 tested irregular particles at the maximum shot noise level of 7.4%. Furthermore, our method has obviously reduced the uncertainty in the 3D shape reconstruction of irregular, rough particles.

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Particle Metrology and Classification Instrumentation Based on Dual-Wavelength Illumination and Modulated Polarization Detection

Li,

Zhao,

2024

IEEE Trans. Instrum. Meas.

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Particle tracking and identification using on-chip holographic imaging with inertial separation

Cited by 5 publications

References 32 publications

Uncertainty elimination in 2D shape reconstruction of irregular particles in Interferometric particle imaging

Uncertainty elimination in 2D shape reconstruction of irregular particles in Interferometric particle imaging

2D shape reconstruction of submillimetric irregular rough particles from speckle pattern in interferometric particle imaging measurement

Particle Metrology and Classification Instrumentation Based on Dual-Wavelength Illumination and Modulated Polarization Detection

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