# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
## 2 The Related Work  
In recent years, with the development of artificial intelligence algorithms, there have been some new attempts to fuse multi-source images. [PERSON] et al. [1] extend the empirical state decomposition to bidimensional empirical mode decomposition. From a new perspective, the application of bidimensional empirical mode decomposition (BEMD) fully demonstrates the advantages of non-stationary time frequency and nonlinear and adaptive capabilities in multi-source image fusion processing. It divides the input signal into different scales, which represent different frequency components of the input. These differentfrequency components are called different frequency layers. It is different from other time frequency signal processing such as discrete Fourier transform [2] and discrete wavelet transform (DWT) [3]. The BEMD method does not rely on any prior basic function, but carries out the adaptive decomposition process through its own data features, and can better represent the signal features in time frequency positioning behavior. In the process of multi-source image fusion based on BEMD by [PERSON] et al. [4], most of the detailed features in the image are extracted into the first bidimensional intrinsic mode function (BIMF), and the remaining detailed features are embedded into subsequent BIMFs and residuals. In the process of image decomposition, the BEMD algorithm is closely related to the correct interpolation operation. Due to the lack of local extremum constraints, incorrect interpolation will appear at the edge of the interpolated image, which is the edge effect in BEMD decomposition. However, the edge effect in BEMD decomposition has not been well solved. After the image is decomposed into multiple BIMFs and residuals, the ideal part is selected for fusion. A pixel-based fusion strategy is used to combine each component of BEMD. Pixel-level fusion rules cannot capture the significant information of the source image well, and will produce fuzzy fusion results. When fusing multi-modal images based on energy maximum selection strategy, the details of fusion result will be lost due to the difference of multi-modal image frequency regarding the selection of image sharpness values for fusion aspects. [PERSON] et al. [5] constructed a novel convolutional neural network based on the Dempster-Shafer theory to deal with the problems of overlapping focus regions and depth of field. The Dempster-Shafer theory is introduced to fuse the results of different branches, which improves the reliability of the results. The gradient residuals are designed to improve the utilization of edge information and reduce the dimension of branch layer feature mapping, thus improving the performance of the network and reducing the number of training parameters. Compared with other advanced fusion methods, the fusion map obtained by this method is more accurate. Considering image sharpness evaluation, a new image sharpness evaluation function, is proposed by [PERSON] [6]. It can enhance the high frequency information of the image by histogram equalization, reduce the influence of jitter artifacts and stray light by DWT, and quantitatively evaluate the sharpness value of the image by calculating the edge gradient with Tenengrad function to obtain the sharpness value of the entire image. [PERSON] et al. [7] designed an algorithm combining the Tenengrad operator and the hill climbing search function for the high-speed autofocus module of UAVs to solve the problem of low noise resistance and real-time performance of the traditional gray gradient algorithm. The UAV focusing system consists of two telescopes and deep learning-based target detection, complemented by suitable linear phase and passive focusing algorithms to achieve fast autofocus. The purpose of the image fusion method mentioned above is to extract all useful features from a given multi-source image, and finally obtain a composite image containing all features of the multi-source image while reducing noise.