Laser & Optoelectronics Progress, Volume. 56, Issue 12, 121002(2019)

USM Sharpening Image Detection Based on Local Binary Pattern Method

Yongzhi Quan, Shuhui Gao*, Mengjing Yang, Xiaojia Jiang, and Xinlong He
Author Affiliations
  • School of Forensic Science, People's Public Security University of China, Beijing 100038, China
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    Figures & Tables(9)
    Flow chart of model algorithm
    LBP feature operator. (a) Original LBP feature operator; (b) improved LBP feature operator
    Rotation invariant LBP pattern
    LBP values of sharpened and unsharpened images
    Extraction process of LBP features. (a) Original image; (b) image of LBP features; (c) normalized histogram of LBP features
    • Table 1. Accuracy of each mode when R=0.5%

      View table

      Table 1. Accuracy of each mode when R=0.5%

      Parameterriu2riu2
      R=0.5, A=0.5908382
      R=0.5, A=1.0928986
      R=0.5, A=1.5938888
    • Table 2. Accuracy of each mode when R=1.0%

      View table

      Table 2. Accuracy of each mode when R=1.0%

      Parameterriu2riu2
      R=1.0, A=0.5939290
      R=1.0, A=1.0969694
      R=1.0, A=1.5979591
    • Table 3. Accuracy of each mode when R=1.5%

      View table

      Table 3. Accuracy of each mode when R=1.5%

      Parameterriu2riu2
      R=1.5, A=0.5999898
      R=1.5, A=1.09910098
      R=1.5, A=1.59910096
    • Table 4. Accuracy under different sharpening parameters%

      View table

      Table 4. Accuracy under different sharpening parameters%

      ParameterR=1.0,A=0.5R=0.7,A=1.0R=1.0,A=1.0R=1.0,A=1.5R=1.3,A=1.5
      Method inRef. [12]81.2585.2092.5594.8396.05
      ri9396979799
      u292949695100
      riu29093949196
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    Yongzhi Quan, Shuhui Gao, Mengjing Yang, Xiaojia Jiang, Xinlong He. USM Sharpening Image Detection Based on Local Binary Pattern Method[J]. Laser & Optoelectronics Progress, 2019, 56(12): 121002

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    Paper Information

    Category: Image Processing

    Received: Nov. 26, 2018

    Accepted: Jan. 10, 2019

    Published Online: Jun. 13, 2019

    The Author Email: Gao Shuhui (gaoshuhui@ppsuc.edu.cn)

    DOI:10.3788/LOP56.121002

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