# Analysis of Blur Measure Operators for Single Image Blur Segmentation
## 4 Results and Discussion
### Qualitative Analysis  
In this subsection, we conduct two types of experiments and exhibit their results through images. In both type of experiments, we randomly select one image from each of the sets of defocus and motion blurred images as their representative example and apply blur measure operators to retrieve their blur maps. In the first experiment, we intend to assess the quality of the responses of blur measure operators at the intermediate steps of blur segmentation process. In addition, in order to evaluate the performances relatively, responses of one blur measure operator can be compared with that of another one at all the intermediate steps. For the defocus blur case, the fourth image of Figure 1 has been selected. The responses of blur measure operators for this image have been shown in Figure 5. It can be observed that the performances for DER08 and DER09 are the best while DER02, DER03, STA02, MIS05 and MIS06 are the worst at all the key steps. The highest scale i.e., \(21\times 21\) has been shown for the initial blur and alpha maps. For the motion blurred case, the fifth image of Figure 1 has been selected and Figure 6 presents its behavior at the steps of segmentation algorithm. It can be noticed that the STA06 produces better results at all the steps while MIS06's performance is again the worst along with DER01, DER05, DER08 and STA02. In addition to the blurriness, images in real scenarios may also get corrupted by the noise too. Therefore, to replicate such a common and vulnerable scenario, we conduct the second experiment to explore the effect of noise along with the blur. For this, two types of noise, salt and pepper and random impulse noise have been considered. This constitutes four scenarios: (1) defocus blur with salt and pepper noise, (2) defocus blur with random impulse noise, (3) motion blur with salt and pepper noise, and (4) motion blur with impulse noise. Further, to explore the robustness of blur measure operators against different levels of noise, three density levels 1%, 10% and 20% for both types of noise have been considered. One image from focus blurred and one image from motion blurred image sets has been selected randomly and has been garbled with the noise. The blur maps produced by the blur measure operators for the four scenarios have been shown in Figures 7-10, respectively. For the first case, Figure 7a displays the selected clean image, its ground truth and its three copies corrupted by salt and pepper noise of density 1%, 10% and 20% respectively. In (b), the blur maps obtained by applying blur measure operators have been shown. The best results are manifested by DER09, DER10; while the worst responses are from DER01 and STA02. Not surprisingly, majority of the operators adapted from the autofocus and SFF techniques demonstrated appreciable results, like DER04 to DER11 and STA06. Further, among different categories, transform-based operators show degraded results in case of noise. In the second case, Figure 8 shows the responses of blur measure operators for the defocus blur and random impulse noise. Figures 9 and 10 display the responses of blur measure operators for the motion blurred image by considering the effect of salt and pepper and impulse noise, respectively. Figure 5: Successive stages of segmentation process for a defocus blurred image. Figure 6: Successive stages of segmentation process for a motion blurred image. Figure 7: Visual comparison of the performance of blur measure operators for defocus blurred image with and without salt and pepper noise. (**a**) From left to right: first, randomly selected image; second, its ground truth blur map; third to fifth, noisy variants of the original image corrupted with salt and pepper noise of density \(N_{d}=1\%\), \(10\%\) and \(20\%\) respectively. (**b**) Blur maps for the clean and noisy images. Figure 8: Visual comparison of the performance of blur measure operators for defocus blurred image with and without random impulse noise.