Acta Optica Sinica, Volume. 43, Issue 22, 2229001(2023)

Deep Learning-Based Particle Shape Classification Using Low-Bit-Depth Speckle Patterns in Interferometric Particle Imaging

Yushi Fu1,2, Hongxia Zhang1,2、*, Jinghui Hou1,2, Dagong Jia1,2, and Tiegen Liu1,2
Author Affiliations
  • 1School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
  • 2Key Laboratory of the Ministry of Education on Optoelectronic Information Technology, Tianjin University, Tianjin 300072, China
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    Figures & Tables(16)
    Process of detecting defocused speckle of irregular particles in IPI system
    Setup of simulated particle IPI system
    Autocorrelation analysis of irregular particle shape. (a) Simulated ice particle shape; (b) autocorrelation calculation result of (a); (c) defocused speckle in simulated particle IPI system; (d) 2D Fourier transform amplitude of (c) binarization result; (e) defocused speckle in experimental particle IPI system; (f) 2D Fourier transform amplitude of (e) binarization result
    Shape categories distribution and corresponding examples of ice particles in ICDC
    Training and testing processes of particle shape classification network
    Defocused speckle and correlation analysis of 5 ice particle shape categories
    Updating strategies of diffuser. (a) Rotating; (b) lateral translating; (c) longitudinal translating; (d) replacing
    Variation in speckle correlation after applying updating strategies
    Structure of DenseNet
    Training loss and accuracy curves of AlexNet, ResNet152, and DenseNet169
    Influence of defocused distance on classification accuracy. (a) Testing set classification accuracies at four different defocused distances; (b) confusion matrix of speckle testing set at defocused distance of 70 mm
    Speckle data at different bit depths. (a) 8 bit; (b) 2 bit; (c) 1 bit
    Influence of speckle bit depth on classification accuracy. (a) Testing set classification accuracies at different bit depths;(b) confusion matrix of 1 bit speckle testing set at defocused distance of 70 mm
    Influence of binarization threshold on classification accuracy of 1-bit speckle data. (a) Grayscale histogram of raw speckle and binarization thresholds. Inset: classification accuracies of speckle data under different thresholds; (b) 1-bit speckle and its sparsity corresponding to different grayscale thresholds
    Influence of speckle pattern size on classification accuracy of speckle data. (a) Speckle slices of different sizes; (b) testing set classification accuracies of different speckle pattern sizes
    • Table 1. Comparison of training parameters for AlexNet, ResNet152, and DenseNet169

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      Table 1. Comparison of training parameters for AlexNet, ResNet152, and DenseNet169

      NetworkAlexNetResNet152DenseNet169
      Training time /s3386.13581.51946.3
      Weight size /MB233.2222.148.6
      Number of parameters /M61.060.227.2
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    Yushi Fu, Hongxia Zhang, Jinghui Hou, Dagong Jia, Tiegen Liu. Deep Learning-Based Particle Shape Classification Using Low-Bit-Depth Speckle Patterns in Interferometric Particle Imaging[J]. Acta Optica Sinica, 2023, 43(22): 2229001

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    Paper Information

    Category: Scattering

    Received: Jun. 25, 2023

    Accepted: Aug. 2, 2023

    Published Online: Nov. 20, 2023

    The Author Email: Zhang Hongxia (hxzhang@tju.edu.cn)

    DOI:10.3788/AOS231180

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