# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
## 5 Experimental Results
### Qualitative Evaluation  
Four sub-categories, namely color multi-focus, gray multi-focus, medical, and infrared and visible images, are conducted for image fusion performance evaluation. We list one image for each category in Figure 8.  
There are four kinds of source image pairs for image fusion evaluation. (a) The first and second row images are image with clear foreground and fuzzy background, and image with fuzzy foreground and clear background. (b) The first and second row images are image with clear foreground and fuzzy background, and image with fuzzy foreground and clear background. (c) Medical images captured from different instruments. (d) The first and second row images are visible and infrared images. Image fusion results of four sub-categories by LP, RP, DWT, DTCWT, CVT, NST, and the proposed method are displayed in Figure 9 to Figure 12, respectively. Figure 8: (a) Color multi-focus, (b) gray multi-focus, (c) medical images, (d) infrared and visible images. It can be seen from Figure 9 that the junction between the foreground and the background, which is shown in detail 1 of the detail figure, is that the algorithms RP, DWT, NXCT, and CVT will have uneven edges and artifacts on the edge of the club, while the Sobel operator in our algorithm can calculate gradients in eight directions. The final sharpness value obtained by combining sharpness weight can solve the unsmooth phenomenon caused by image fusion. In the final graph fusion strategy, the segmentation and overlapping strategy will reduce the appearance of spatial artifacts. As shown in detail 2 in Figure 9, other algorithms at the collar of the golf ball perform less well than the improved algorithm. Figure 10 is the result of gray multi-focus image fusion. It can be seen that obvious speckle noise appears in detail 1 after the fusion of the RP algorithm, which has a great impact on the quality of the fusion image. In the DWT algorithm, fringe noise appears, and other algorithms will more or less appear as junction blurring and non-smoothing phenomena, while our algorithm smooths the noise that may occur in the fusion process through Gaussian filtering. After smoothing the image, the improved Sobel operator can effectively calculate the image sharpness, making the final fusion image detail features more obvious. As can be seen from detail 2, there is an obvious artifact at the border of the clock. The reason for this situation is that the block with higher graphic definition cannot be effectively selected for fusion during image fusion. Figure 11 is the detail diagram of multi-modal medical image fusion. The algorithms RP, LP, DTCWT CVT, and NXCT show blocky segmentation and unsmooth edges in detail 1 and 2. We use Gaussian filtering to reduce noise and unsmooth problems caused by interpolation. In image fusion, the method of overlapping segmentation is used to make the edge of the image smoother. Figure 12 shows that the RP algorithm of infrared image and visual image has serious detail loss, the DWT algorithm has a breakpoint at the body, and the human brightness of the LP, DTCWT, CVT, and NXCT algorithms is not obvious. Our algorithm makes up for these shortcomings by weighting the sharpness value to improve the brightness of the fusion image, and salient features become significantly brighter, such as the car and the pedestrian, and the brightness information is smooth. There is less noise. The contours and details of the object have fewer splicing marks, and the texture is fully preserved. Figure 9: Comparison of different algorithms for color multi-focus image fusion. (**a**) LP algorithm, (**b**) RP algorithm, (**c**) DWT algorithm, (**d**) DTCWT algorithm, (**e**) CVT algorithm, (**f**) NXCT algorithm, (**g**) our algorithm. Figure 11: Comparison of different algorithms for medical multi-model image fusion. (**a**) LP algorithm, (**b**) RP algorithm, (**c**) DWT algorithm, (**d**) DTCWT algorithm, (**e**) CVT algorithm, (**f**) NSTST algorithm, (**g**) our algorithm.