# On-Board Flickering Pixel Dynamic Suppression Method Based on Multi-Feature Fusion
## 3 Experimental Results and Discussion
### Results and Discussion  
According to the experimental data and evaluation criterion above, the proposed method is compared with the scene-based bad pixel dynamic correction (SBBPDC) method [13] and the calibration-based adaptive threshold bad pixel detection (CBATBPD) method [9]. In this experiment, we used the median filter and threshold segmentation to detect IRPT and use the interframe matching to track IRPT, in which our proposed method was embedded. The above IRPT process methods are one of the most common and the simplest methods in IRPT application. It is necessary to point out that our proposed method can also be embedded in any IRPT detection and tracking method. Figure 7 shows the blind pixel maps of the first sequence of four scenarios, which are detected by SBBPDC, CBATBPD, and our proposed method. The top row to bottom row of Figure 7 is sky background, sky background with IRPT, deep space background, and deep space background with IRPT, respectively. The left column to right column of Figure 7 is the proposed method, SBBPDC, and CBATBPD, respectively. The blue points denote the blind pixels detected by our method. The red triangular rims and green square rims represent the blind pixels that are not detected by SBBPDC and CBATBPD, respectively. It is clear in the left column of Figure 7 that our method has detected many blind pixels that are not detected by other methods. Similarly, the red triangles and green squares denote the blind pixels detected by SBBPDC and CBATBPD, respectively, and the blue round rims are the blind pixels that are miss detected by our method. Obviously, there are only a few blind pixels that cannot be detected by the proposed method compared with other methods. As the middle column of Figure 7 shown, the SBBPDC cannot detect some adjacent blind pixels compared with our proposed method. This is because the adaptive threshold segmentation of this method erroneously suppresses a part of adjacent blind pixels. Furthermore, due to the movement of the background, some edges of the image usually move out quickly and do not have the same part for inter-frame registration. Therefore, the CBATBPD cannot detect the blind pixels at some edges of the field of view as shown in Figure 7. In conclusion, our method adopts multi-feature to accurately identify the real targets from potential targets. It can detect more real blind pixels compared with other methods. The blind pixel maps of other sequences have similar results as the first sequence. However, space does not permit their presentation. In Figure 8, we respectively plotted the number of blind pixels detected by the above-mentioned methods in each frame. Figure 8a-d illustrates the data of the first sequence of sky background, sky background with IRPT, deep space background, and deep space background with IRPT, respectively. It is obvious that the proposed method can detect more blind pixels with fewer frames compared with other methods. SBBPDC uses the keyframe to improve the detection accuracy and efficiency, which depends on the change of background. Therefore, the detection rate of SBBPDC tends to slow down in deep space because the change of background is not obvious. Additionally, CBATBPD needs hundreds of frames to ensure detection accuracy. Subsequently, we calculate the \(N_{bp}\), \(R_{fbp}\), and \(T_{pf}\) mentioned in Section 3.2 of the above three methods in Table 3. The false detection rate of the proposed method is less than 1.02% in different scenarios, while the average running time per frame of the proposed method is less than 0.46 s. The proposed method could detect more blind pixels with less false detection rate and less time. It can be clearly seen that the proposed method is superior to others in four application scenarios.