# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 2 Related Work  
Exposure fusion was first proposed by [PERSON] et al. [10]. In this method, each input image is decomposed into a Laplace pyramid and a Gauss pyramid of weight map is established for each image according to contrast, saturation, and exposure, thus realizing a multi-resolution fusion framework. Multi-resolution fusion methods achieve good visual results in the multi-exposure images of most scenes but lose valid details in brighter and darker areas. [PERSON] et al. [11] proposed a fusion method based on smoothing filtering, which divides low dynamic range images into blocks and uses entropy to calculate the weight of each block, and then uses the block with the highest weight at each location to construct the resulting image; however, the boundaries between image blocks will appear unsmooth. In order to smooth these boundaries, a two-dimensional Gaussian filter is deployed. In this method, smaller blocks have a better smoothing effect than larger blocks but lose computation time, so there is a trade-off between smoothness and computational time complexity. [PERSON] et al. [12] proposed a generalized random walk framework that comprehensively considers neighborhood information, local contrast, and color consistency and achieves a globally optimal probabilistic model. [PERSON] et al. [13] inversely transform and linearly stretch the gradient field by maximizing the structure tensor in the gradient field. However, the fused image suffers from color blurring and unnatural phenomena. [PERSON] et al. [14] used guided filtering to decompose the source image into base and detail layers, performed a weighted fusion of the base and detail layers determined by the average level of local brightness changes, and implemented a low-level feature-basedmulti-exposure fusion algorithm. This method performs well in image sharpness and color information preservation but is prone to noise and halos. Although these traditional multi-exposure fusion schemes can achieve good visual effects, there is a trade-off between image details and brightness. If the brightness weight is considerable, the fusion image will be brighter but some details will be lost; conversely, a certain amount of luminance weight is lost to highlight more texture details. In recent years, people have also introduced detail enhancement mechanisms in multi-exposure fusion methods. However, most of them introduce a detailed extraction mechanism based on edge-preserving filters, such as a bilateral filter [15], a weighted least-squares filter (WLS) [16], a guided filter [17], and other improved filters. [PERSON] et al. [18] used the difference between the original pixel value and the bilateral filtered pixel value to determine the weight value of each pixel. Although it can retain strong edges, it performs poorly in global contrast and color visibility. [PERSON] et al. [19] proposed a structural block decomposition-based MEF algorithm (SPD-MEF) but the structural block decomposition is time-consuming. [PERSON] et al. [20] used edge-preserving recursive filtering techniques to reduce near-edge artifacts. [PERSON] et al. [21] made full use of the spatial consistency to fuse the base and detail layers through a weighted-average technique based on guided filtering. [PERSON] et al. [22] utilized a gradient domain-based weighted least-squares method to extract details of the brightest and darkest regions. However, most of these multi-exposure image fusion algorithms that are based on detail enhancement enhance the details of all pixels except the brightest and darkest areas. This processing method increases the complexity of the algorithm and leads to the excessive enhancement of normal pixel details.