Infrared and Laser Engineering, Volume. 51, Issue 3, 20210798(2022)

Dynamic real-time restoration algorithm of defective pixels based on spatio-temporal statistics feature

Dezhen Yang1,2, Songlin Yu1, and Jinjun Feng2
Author Affiliations
  • 1North China Research Institute of Electro-optics, Beijing 100015, China
  • 2Beijing Vacuum Electronics Research Institute, Beijing 100015, China
  • show less
    Figures & Tables(13)
    Blind element 15×15 neighborhood gray distribution on clean background
    Flow chart of the proposed algorithm
    Local extremum operator
    Defective pixel location based on extremum operator and three-layer pyramid
    FPGA logic implementation flow of the proposed algorithm
    Timing simulation result of hardware implementation
    Target and defect element sequence set in multiple scenarios
    Comparison of FPGA implementation using different methods. (a) Original image output by the detector; (b) Multi-scale adaptive median filter; (c) Local contrast method based on saliency; (d) Structural elements and 3σ criterion; (e) Proposed algorithm
    Diagram of the performance improvement for point target detection. (a) Multiscene infrared point target detection; (b) Target neighborhood; (c) 3 D image of grayscale distribution of point target neighborhood; (d) Target neighborhood with the proposed algorithm; (e) 3 D image of the grayscale distribution of point target neighborhood with the proposed algorithm
    • Table 1. Comparison of temporal and spatial gray distribution characteristics of defect element, target and clean background

      View table
      View in Article

      Table 1. Comparison of temporal and spatial gray distribution characteristics of defect element, target and clean background

      TypeGray distribution of spatialGray distribution of temporal
      Over hot pixel
      Dead pixel
      Flickering pixel
      Defect pixel cluster
      Point target
      Background
    • Table 2. Comparison of BSF of different algorithms

      View table
      View in Article

      Table 2. Comparison of BSF of different algorithms

      ResolutionDevice materials BandFrame-rate/ fps Integrated-time/ ms
      640×512MCTMid-wave1002.8-7.5
      640×512MCTLong-wave3000.3-1.2
      1 k×1 kMCTMid-wave753-8
      256×256ISMid-wave750.5-3.6
      64×64ISLong-wave5000.2-0.5
      640×512ISLong-wave1000.8-5
      320×256SLsLong-wave750.5-1.8
    • Table 3. Effects and problems of different algorithms for eliminating isolated pixel, flickering pixel and defective pixel clusterss

      View table
      View in Article

      Table 3. Effects and problems of different algorithms for eliminating isolated pixel, flickering pixel and defective pixel clusterss

      AlgorithmIsolated blind pixel Isolated flickering pixel Defect pixel cluster Timing summary/ms Existing problems
      AMFValidValidValid0.325Background detail information are lost; Dim target will be removed
      LCMValidInvalidInvalid0.830Dim target will be removed
      SE-3σValidInvalidInvalid3.421Dim targets may be removed;Algorithm running time is a little long
      OCSVMValidInvalidInvalid--Difficult to implement on FPGA; Dim target may be removed
      DDABSValidValidInvalid0.326Unable to adjust time-consuming flexibly
      ProposedValidValidValid0.329N/A
    • Table 4. Comparison of detection performance of different algorithms in three kinds of scenes

      View table
      View in Article

      Table 4. Comparison of detection performance of different algorithms in three kinds of scenes

      AlgorithmClean backgroundGround backgroundComplex cloud background
      SCRgBSFPFgDARSCRgBSFPFgDARSCRgBSFPFgDAR
      Proposed1.211.362.340.981.451.638.210.953.151.928.920.95
      AMF0.121.816.430.520.312.723.240.490.323.322.930.48
      LCM1.161.231.890.830.841.281.420.760.781.761.390.73
      SE-3σ1.191.322.180.871.341.362.320.851.451.521.730.81
      OCSVM1.031.321.350.761.161.291.240.741.131.471.160.77
      DDABS1.211.361.920.911.421.544.210.882.381.745.210.82
    Tools

    Get Citation

    Copy Citation Text

    Dezhen Yang, Songlin Yu, Jinjun Feng. Dynamic real-time restoration algorithm of defective pixels based on spatio-temporal statistics feature[J]. Infrared and Laser Engineering, 2022, 51(3): 20210798

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image processing

    Received: Dec. 20, 2021

    Accepted: --

    Published Online: Apr. 8, 2022

    The Author Email:

    DOI:10.3788/IRLA20210798

    Topics