# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
## 6 Discussion and Conclusions  
Image fusion is a hot research topic in the fields of medicine, machine vision, photography nowadays. The work of this article is an attempt of multi-focal and multi-modal image fusion. Our method can preserve important image features and rich detailed information. All kinds of fusion methods play a role in the algorithm and improve the quality of image fusion. In our approach, image quality is improved at each stage. Our algorithm decomposes the image into IMFs and residuals via BEMD. At this time, the image obtained by IMFs and residual after interpolation process is noisy. We remove the noise and smooth the image by Gaussian filtering. In the fusion process, the improved eight-direction Sobel operator is combined with the weighting function to calculate the sharpness value. The sharpness obtained is the key to the next step of fusion, and the fusion strategy of segmentation and overlap is selected in the final fusion strategy, which can make the image edges smoother and the obtained image details more obvious. All in all, from a visual point of view, it can be seen from Figures 9-12 and Tables 2-5 that the fusion image quality produced by our proposed method is superior to other fusion methods, which proves the superiority of our method. While the proposed work shows advantages over recent fusion approaches, the work still has limitations. The proposed work has the best performance in multi-focal image fusion, but the performance is slightly worse in multi-modal image fusion. And the calculation time of this method is not ideal. In future studies, we will emphasize reducing time consumption while maintaining improved fusion image quality. At the same time, our approach is still likely to continue to improve performance in the following aspects. Our method can reduce artifacts in multi-focus image boundary region fusion, as shown in Figure 9. In multi-focal image fusion, this is an open question, and a recent study [23] has given us a lot of inspiration in this regard. Such problems can be reduced by means of image enhancement. In the aspect of multi-modal image fusion, contrast, illumination, noise, and other factors have a great impact on the accurate judgment of the image, and a preprocessing method and image enhancement [24; 25] can improve the imaging effect. The pre-processing of these two data sets will be the main direction of our subsequent improvement. Conceptualization, X.-T.G. and X.-J.D. ; methodology, X.-T.G. and X.-J.D. ; software, X.-T.G. and X.-J.D. ; validation, X.-T.G., X.-J.D. and H.-H.K. ; formal analysis, X.-T.G. and X.-J.D. ; investigation, X.-J.D. ; resources, X.-T.G. ; data curation, X.-J.D. ; writing--original draft preparation, X.-J.D. ; writing--review and editing, X.-J.D. ; visualization, X.-J.D. ; supervision, X.-T.G. and H.-H.K. ; project administration, X.-T.G. and H.-H.K. ; funding acquisition, X.-T.G. and H.-H.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the Fundamental Research Program of Shanxi [202103021223194], and Shanxi Key Laboratory of Signal Capturing and Processing.