# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 4 Results and Analysis
### Objective Analysis  
Objective indicators are very important for evaluating the performance of fusion algorithms. To objectively evaluate the fusion results, we adopt the image quality assessment  
Figure 14: Pixel intensity analysis of the zoom-in patches in Figure 10 along the horizontal direction. (**a**) Comparison of eight methods. (**b**) Enlarged image of the data in the black box in (**a**). Figure 13: Comparison of fusion results for Figure 9d. (**a**) SPD-MEF method fusion result. (**b**) MRF method fusion result. (**c**) DSIFT method fusion result. (**d**) FEAF method fusion result. (**e**) B-LP method fusion result. (**f**) LLF method fusion result. (**g**) The proposed method fusion result. (**a1**–**g1**) represent magnified images of the red area in (**a–g**). (**a2**–**g2**) represent magnified images of the yellow area in (**a–g**). (IQA) proposed by [PERSON] et al. [26] to evaluate the image quality of human visual perception. IQA scores range from 0 to 1, the higher values indicating better image quality. Using the gradient-based quality index \(Q^{AB/F}\) proposed by [PERSON] et al. [37], the fusion performance is evaluated by measuring the guidance degree of the source image's gradient information against that of the fusion image. The larger the value of \(Q^{AB/F}\), the better the fusion effect. We also use a non-reference quality metric NIQE to evaluate the possible losses of 'naturalness' in a fused image. The performance of the state-of-the-art and the proposed method is evaluated by the above objective metrics. Table 2 presents the quantitative evaluation results of the different fusion methods of the IQA in the MEF dataset and the best fusion results are shown in bold. As can be seen in Table 2, our fused images have the highest human visual perception scores in 10 of the total 16 sets and are only slightly lower than the DSIFT and LLF in a few test sets. Although the algorithm in this paper is slightly lower than other algorithms in several sets of test data sets, it can retain more details in regions with large exposure differences. As shown in Table 3, among the 16 groups of comparison results, the \(Q^{AB/F}\) average value of the algorithm in this paper is the largest. \(Q^{AB/F}\) can evaluate the edge information transmitted from the input image to the fusion. The test results show that the proposed algorithm performs well in edge preservation. According to Table 4, the algorithm in this paper achieves lower NIQE values in 10 test datasets. From the experimental results, the algorithm in this paper can maintain a low loss of color information and detailed information. In other words, in most cases, the natural loss of our fusion results is lower than that of other algorithms. In general, the algorithm performs well in terms of visual effects, detail preservation, and reduction of image information loss. In order to verify the performance of the method, we calculate the running time of the existing seven methods and the proposed method on 3 test sets named \"Landscape,\" \"Lamp,\" and \"Madison Capital.\" The results are shown in Table 5. The running time of our algorithm is greatly improved compared to the B-LP method. From the experimental results, when a set of datasets contains a small number of multi-exposure images, the algorithm's running time is not much different from other algorithms. When the number of multi-exposure images is large, the processing speed of the algorithm in this paper is slow. Although the running time of the proposed algorithm is slightly inferior to other algorithms, it is also at a low level. On the other hand, the superiority of the algorithm in this paper is also reflected in the subjective and objective analyses.