# Analysis of Blur Measure Operators for Single Image Blur Segmentation
## 4 Results and Discussion
### Quantitative Analysis  
In the previous subsection, blur measure operators have been described according to their performance when applied on a randomly selected blurred image. However, it does not seem qualified to compare the performance of the blur measure operators on the basis of just a single image. As it is quite possible that the evaluation performances might have been influenced by the particular choice of the image, such that, if some other image might had been selected for analysis, performance ranking of operators might had been different. Therefore, now, we intend to investigate the possibility that whether the ranking of operators is dependent on the choice of the image or not. Put another way, this is to explore the validity of general notion that diverse nature of image content and the type and degree of blur may favor certain type of operators. We select considerable number of images from each of the defocus and motion blurred image sets separately and consider them to be the representative subset of their category. These subsets instead of a single image serve as a better instance to integrate and comprehend the diverse range of image content and level of blurriness. The blur segmentation methodology of Section 3.2 has been applied on these subsets and their results have been compared with their respective ground-truths. The quantitative measures used for the evaluation of performance have been described in Section 3.3. The value of \(\beta\) in Equation (9) for the computation of f-measure has been set to be as 0.3. The evaluation measures for all the images in the subset, in both of the defocus and motion blur cases separately, have been combined together to give their average values. These average values are displayed in the bar graphs for the comparison of blur measure operators. The responses of the categories of blur measure operators are also shown in the sub-figures. These category evaluation measures are computed such that the responses of blur measure operators belonging to the same category have been combined together to give the average response for that category. Figure 11 represents the average values of evaluation measures for blur measure operators for the set of 303 images affected by the defocus blur. Highest precision and F-measure are achieved by TRA04 and then TRA05. Those who got highest recall are DER09 and then DER10. Lowest values of precision, and F-measure are attained by DER05 and then DER01. Figure 12 shows the average performance for the 204 defocused blurred images when salt and pepper noise with 1% density is added to them. Highest precision and F-measure are achieved by STA04 and then STA01. Highest recall is for DER09 and then DER08. While the lowest precision, recall and F-measure are exhibited by DER02. While comparing the average responses of categories of blur measure operators in Figures 11 and 12, it reveals that miscellaneous operators show robustness against noise while the derivative-based operators get degraded highly in case of noise. While, Figure 13 demonstrates the average performance of blur measure operators for the set of 235 images affected by motion blur, Figure 14 shows the average values of evaluation measures for the 296 motion blurred images corrupted by salt and pepper noise of density 1%. It can be seen that STA01 achieves the best results in terms of precision and F-measure, while DER08 is at the top and DER09 is second for recall. The worst values for all three evaluation measures are exhibited by DER02. However, again on average, the best performance is shown by transform-based operators. In Figures 12 and 14 it can be easily identified that DER02 offers the least robustness against noise. Hence, it is important to mention that the ranking for the performance of blur measure operators is highly influenced by the choice of the image under consideration. That is, if a different image is selected, the operator which appeared somewhere down in the previous performance ranking may now surface up, even to the top, in the new ranking.