# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
## 5 Experimental Results
### Qualitative Evaluation
Figure 10: Comparison of different algorithms for gray multi-focus image fusion. (**a**) LP algorithm, (**b**) RP algorithm, (**c**) DWT algorithm, (**d**) DTCWT algorithm, (**e**) CVT algorithm, (**f**) NST algorithm, (**g**) our algorithm. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **MI** & **FMI** & **PSNR** & **SD** & **Q\({}^{\mathbf{Y}}\)** & **Q\({}^{\mathbf{VIF}}\)** & **TIME** \\ \hline LP & 0.7116 & 0.8965 & 31.9464 & 9.4535 & 0.8205 & 0.9016 & 0.0570 \\ \hline RP & 0.7171 & 0.8689 & 30.6170 & 9.4336 & 0.7635 & 0.8500 & 0.0259 \\ \hline DWT & 0.6736 & 0.8828 & 31.8465 & 9.3930 & 0.7918 & 0.7842 & 0.0755 \\ \hline DTCWT & 0.6272 & 0.8928 & 31.7666 & 9.4326 & 0.6999 & 0.7896 & 0.1352 \\ \hline CVT & 0.5818 & 0.8873 & 31.4522 & 9.4282 & 0.5682 & 0.7174 & 0.7658 \\ \hline NSCT & 0.6685 & 0.8929 & 32.0096 & 9.4237 & 0.7483 & 0.8430 & 1.6713 \\ \hline Ours & 0.7819 & 0.8984 & 32.1865 & 9.5780 & 0.9616 & 0.9579 & 1.1853 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results of different algorithms in medical multi-modal image set. Figure 13: Quantitative results of our method with six representative methods and six evaluation indicators on the widely used dataset. Notably, the red dotted lines describe the results of our method. The 54 image pairs are sequentially color and gray multi-focus, medical multi-mode, and infrared and visible multi-mode images. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **MI** & **FMI** & **PSNR** & **SD** & **Q\({}^{\mathbf{Y}}\)** & **Q\({}^{\mathbf{VIF}}\)** & **TIME** \\ \hline LP & 0.9519 & 0.9024 & 35.7084 & 9.7644 & 0.9264 & 1.5326 & 0.0315 \\ \hline RP & 0.9405 & 0.8964 & 34.8109 & 9.7586 & 0.9148 & 1.5020 & 0.0916 \\ \hline DWT & 0.8481 & 0.8970 & 34.9988 & 9.7636 & 0.8883 & 1.4222 & 0.1350 \\ \hline DTCWT & 0.8969 & 0.9015 & 35.4490 & 9.7546 & 0.9260 & 1.4885 & 0.3092 \\ \hline CVT & 0.9476 & 0.9004 & 35.3754 & 9.7594 & 0.9025 & 1.4694 & 1.7779 \\ \hline NSCT & 0.9138 & 0.9009 & 35.5876 & 9.7618 & 0.9275 & 1.5062 & 4.8504 \\ \hline Ours & 1.0506 & 0.8897 & 35.7860 & 10.1688 & 0.9616 & 1.5071 & 0.3329 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of different algorithms in gray multi-focus image set. For the color multi-focus image set (Table 2), the six indicators of the proposed method are optimal. This is consistent with the intuitive effect. For the gray multi-focus set (Table 3), the proposed method is superior to other algorithms in texture detail and sharpness. Thus, MI, PSNR, SD, and \(\text{Q}^{\text{Y}}\) are better. Compared with color images, edge features of grayscale images are weak. The proposed algorithm extracts more feature information from GBIMFs and less background information, resulting in unsatisfactory FMI and \(\text{Q}^{\text{VIF}}\) indicators. For the medical image set (Table 4), the proposed algorithm is optimal in all six indicators, which reflects the effectiveness of the significant feature extraction effect, and it is also clearer and more comfortable from the visual perspective, as displayed in Figure 11.  
For the infrared and visual images set (Table 5), the six indicators of the proposed algorithm except FMI are optimal. The FMI indicator is deficient in mutual feature information after fusion due to the small difference of background environment. This is because, in the process of using BEMD to guide significant feature extraction, some background information is lost due to interpolation and iteration. However, our method still highlights the significance target well, only the richness of feature information needs to be further improved. As for the time index, the time performance of all data sets is not optimal, which is related to the weighted overlap fusion strategy used in the algorithm, and the final fusion image can only be obtained by repeated calculation of the comparative sharpness value. The advantage of the algorithm in time is reduced.