Infrared and Laser Engineering, Volume. 51, Issue 3, 20210798(2022)
Dynamic real-time restoration algorithm of defective pixels based on spatio-temporal statistics feature
Figures & Tables(13)
Fig. 1. Blind element 15×15 neighborhood gray distribution on clean background
Fig. 4. Defective pixel location based on extremum operator and three-layer pyramid
Fig. 7. Target and defect element sequence set in multiple scenarios
Fig. 8. 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 3
Fig. 9. 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 tableView in ArticleTable 1. Comparison of temporal and spatial gray distribution characteristics of defect element, target and clean background
Type Gray distribution of spatial Gray 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 tableView in ArticleTable 2. Comparison of BSF of different algorithms
Resolution Device materials Band Frame-rate/ fps Integrated-time/ ms 640×512 MCT Mid-wave 100 2.8-7.5 640×512 MCT Long-wave 300 0.3-1.2 1 k×1 k MCT Mid-wave 75 3-8 256×256 IS Mid-wave 75 0.5-3.6 64×64 IS Long-wave 500 0.2-0.5 640×512 IS Long-wave 100 0.8-5 320×256 SLs Long-wave 75 0.5-1.8
Table 3. Effects and problems of different algorithms for eliminating isolated pixel, flickering pixel and defective pixel clusterss
View tableView in ArticleTable 3. Effects and problems of different algorithms for eliminating isolated pixel, flickering pixel and defective pixel clusterss
Algorithm Isolated blind pixel Isolated flickering pixel Defect pixel cluster Timing summary/ms Existing problems AMF Valid Valid Valid 0.325 Background detail information are lost; Dim target will be removed LCM Valid Invalid Invalid 0.830 Dim target will be removed SE-3σ Valid Invalid Invalid 3.421 Dim targets may be removed;Algorithm running time is a little long OCSVM Valid Invalid Invalid -- Difficult to implement on FPGA; Dim target may be removed DDABS Valid Valid Invalid 0.326 Unable to adjust time-consuming flexibly Proposed Valid Valid Valid 0.329 N/A
Table 4. Comparison of detection performance of different algorithms in three kinds of scenes
View tableView in ArticleTable 4. Comparison of detection performance of different algorithms in three kinds of scenes
Algorithm Clean background Ground background Complex cloud background SCRg BSF PFg DAR SCRg BSF PFg DAR SCRg BSF PFg DAR Proposed 1.21 1.36 2.34 0.98 1.45 1.63 8.21 0.95 3.15 1.92 8.92 0.95 AMF 0.12 1.81 6.43 0.52 0.31 2.72 3.24 0.49 0.32 3.32 2.93 0.48 LCM 1.16 1.23 1.89 0.83 0.84 1.28 1.42 0.76 0.78 1.76 1.39 0.73 SE-3σ 1.19 1.32 2.18 0.87 1.34 1.36 2.32 0.85 1.45 1.52 1.73 0.81 OCSVM 1.03 1.32 1.35 0.76 1.16 1.29 1.24 0.74 1.13 1.47 1.16 0.77 DDABS 1.21 1.36 1.92 0.91 1.42 1.54 4.21 0.88 2.38 1.74 5.21 0.82
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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
Paper Information
Category: Image processing
Received: Dec. 20, 2021
Accepted: --
Published Online: Apr. 8, 2022
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