• Acta Optica Sinica
  • Vol. 40, Issue 7, 0711003 (2020)
Zhaoyang Yin1, Dezhi Zhang1, Linjie Zhao1、2, Mingjun Chen1、*, Jian Cheng1、**, Xiaodong Jiang2, Xinxiang Miao2, and Longfei Niu2
Author Affiliations
  • 1School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
  • 2Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, Sichuan 621900, China
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    DOI: 10.3788/AOS202040.0711003 Cite this Article Set citation alerts
    Zhaoyang Yin, Dezhi Zhang, Linjie Zhao, Mingjun Chen, Jian Cheng, Xiaodong Jiang, Xinxiang Miao, Longfei Niu. A Dark-Field Detection Algorithm to Detect Surface Contamination in Large-Aperture Reflectors[J]. Acta Optica Sinica, 2020, 40(7): 0711003 Copy Citation Text show less
    Dark field detection schematic diagram
    Fig. 1. Dark field detection schematic diagram
    Auto-focus process
    Fig. 2. Auto-focus process
    Sharpness evaluation function curve
    Fig. 3. Sharpness evaluation function curve
    Calibration board
    Fig. 4. Calibration board
    Linear distorted projection model
    Fig. 5. Linear distorted projection model
    Linear distortion correction model
    Fig. 6. Linear distortion correction model
    Comparison before and after top hat transformation. (a) Grayscale map of original image; (b) grayscale map of transformation results
    Fig. 7. Comparison before and after top hat transformation. (a) Grayscale map of original image; (b) grayscale map of transformation results
    Autofocus curve. (a) Tenengrad function curve of coarse adjustment; (b) focusing motor position curve of accurate adjustment; (c) sub-window Tenengrad function curve of accurate adjustment
    Fig. 8. Autofocus curve. (a) Tenengrad function curve of coarse adjustment; (b) focusing motor position curve of accurate adjustment; (c) sub-window Tenengrad function curve of accurate adjustment
    Image contrast before and after autofocusing. (a) Image before autofocusing; (b) image after autofocusing
    Fig. 9. Image contrast before and after autofocusing. (a) Image before autofocusing; (b) image after autofocusing
    Binarization algorithm comparison. (a) Original image; (b) Otsu algorithm; (c) maximum entropy model; (d) mean weighted adaptive binarization algorithm; (e) Laplacian weighted adaptive binarization algorithm
    Fig. 10. Binarization algorithm comparison. (a) Original image; (b) Otsu algorithm; (c) maximum entropy model; (d) mean weighted adaptive binarization algorithm; (e) Laplacian weighted adaptive binarization algorithm
    Results of ultra-depth microscope. (a) Microscopic image of area 1; (b) microscopic image of area 2
    Fig. 11. Results of ultra-depth microscope. (a) Microscopic image of area 1; (b) microscopic image of area 2
    Evaluation functionVarianceTenengradLaplaceBrennerInformation entropyRoberts
    Sharpness ratio2.08516.27753.47715.16532.51194.1152
    Steepness/10-45.27558.28457.04557.96856.23157.4850
    Fluctuation0.07100.02680.06520.00540.02030.0387
    Table 1. Sharpness evaluation function performance indicators
    ParemeterOtsuMaximum entropyMean weighted adaptive thresholdLaplace weighted adaptive threshold
    Running time /ms2262232037294
    Number error /%-62347
    Table 2. Performance comparison of pollutant extraction algorithms
    Zhaoyang Yin, Dezhi Zhang, Linjie Zhao, Mingjun Chen, Jian Cheng, Xiaodong Jiang, Xinxiang Miao, Longfei Niu. A Dark-Field Detection Algorithm to Detect Surface Contamination in Large-Aperture Reflectors[J]. Acta Optica Sinica, 2020, 40(7): 0711003
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