# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 4 Results and Analysis  
In this paper, the proposed multi-exposure image fusion method is validated on the MEF dataset, as shown in Table 1. The fusion results of our method are compared with seven recently proposed fusion algorithms: median and recursive filtering-based fusion (MRF) [25], structural block-based fusion (SPD-MEF) [19], linear combination-based localexposure weights (LE) [26], boosted Laplacian pyramid-based fusion (B-LP) [27], a fusion technique using a dense SIFT descriptor and guided filter (DSIFT) [28], a fusion method based on low-level features (LLF) [14], and image fusion based on feature evaluation with adaptive factors (FEAF) [7]. Four representative sets of the MEF dataset were selected for presentation in Figure 9. Figures 10-12 show the results of these four sets of images fused with different methods. The proposed method can achieve good visual effects for image sequences with different exposures both indoors and outdoors through subjective and objective analysis. It also retains more valuable details in both the brightest and darkest areas. All experiments were run with MATLAB R2019a on a computer with a Core i5-11400F @1.6 GHz processor and 16.00 GB RAM. Although the MRF method has achieved good visual effects, it has the phenomenon of excessive unnatural brightness and unclear boundaries; as shown in Figure 10b, there are shadows around the lights and the cloud contours in the sky in Figures 11 and 12b are not clear. Figure 11: Comparison of fusion results for Figure 9b. (**a**) SPD-MEF method fusion result. (**b**) MRF method fusion result. (**c**) DSIFT method fusion result. (**d**) FEAF method fusion result. (**e**) LE method fusion result. (**f**) B-LP method fusion result. (**g**) LLF method fusion result. (**h**) The proposed method fusion result. Figure 10: Comparison of fusion results for Figure 9a. (**a**) SPD-MEF method fusion result. (**b**) MRF method fusion result. (**c**) DSIFT method fusion result. (**d**) FEAF method fusion result. (**e**) LE method fusion result. (**f**) B-LP method fusion result. (**g**) LLF method fusion result. (**h**) The proposed method fusion result. DSIFT uses dense SIFT descriptors to compute contrast and smoothing weights for guided filtering, resulting in images with good contrast but easily lost details. As shown in Figure 10c, the details of the top area of the road are lost. In Figure 11c, the details of the cloud texture in the sky are lost and the clarity is poor. The FEAF method can ensure the consistency of the local brightness of the fused image and the input image, but when the number of input images is large, the overall brightness will be darker and the contrast will decrease, as shown in Figure 10d. Although this method can retain some detailed information, it will still lose details when the image brightness difference is large. Figures 11 and 12d show that the clouds in the sky lose the texture details. The fusion results of the LE method and the B-LP method are distorted. As shown in Figure 10e,f, there is much false information and the image has a lot of halo and noise. It can be seen in Figures 11f and 12f that the B-LP method is more practical than the LE method but the color information is lost in Figure 12f and the clouds in the sky appear as blank areas. The LLF method performs well in image clarity and color saturation but it is prone to noise and loss of details at the junction of light and dark. Figure 12g shows that the clouds have poor contrast and unclear outlines. Compared with the other seven methods, the algorithm proposed in this paper shows advantages in all aspects. As shown in Figure 10h, the areas on the top of the road's head with large changes in light and shade have rich texture details and bright colors. In Figure 11h, the outline of the cloud is clear and the boundary has no halo. The sunset in Figure 12h has better color saturation and no loss of color information.