Fig. 1. Blind element 15×15 neighborhood gray distribution on clean background

Fig. 2. Flow chart of the proposed algorithm

Fig. 3. Local extremum operator

Fig. 4. Defective pixel location based on extremum operator and three-layer pyramid

Fig. 5. FPGA logic implementation flow of the proposed algorithm

Fig. 6. Timing simulation result of hardware implementation

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 3σ criterion; (e) Proposed algorithm

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

Table 2. Comparison of BSF of different algorithms

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

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