# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 1 Introduction  
For the past few decades, generating high dynamic range (HDR) images from a group of low dynamic range (LDR) images with different exposures has been a challenge [1]. Low dynamic range images taken with mobile phones and cameras cannot accurately reflect the dynamic range of the real world, mainly because the image sensors in mobile phones and cameras have limited response to the dynamic range of natural light [2], so they cannot capture all the details of the light and dark areas in the dynamic scene. In the early stage of development, many researchers proposed an HDR generation scheme to solve this problem using the camera response function (CRF) and the exposure time information of the input image to linearly map the pixel value of the scene brightness [1] to generate HDR images in line with the visual quality. However, this approach is difficult to determine the CRF because researchers need to calculate the CRF each time they change the capturing device or adjust the parameters (exposure time, IOS, etc.) [3]. In addition, after the HDR image is acquired, the range of the HDR image is compressed using hue mapping to make it display on the monitor [4]. Since tone mapping is time-consuming and requires many steps to complete, it limits the applicability of this method. Therefore, the new concept of multi-exposure fusion (MEF) is proposed. MEF technology is a fusion technology that can fuse images with different exposure times according to fusion rules to generate HDR images that meet the visual quality without any imaging information and can be displayed directly on the display [5]. Generally, MEF methods are divided into two categories: traditional MEF methods and deep learning-based MEF methods. The traditional MEF method technology is very mature, and many researchers have proposed many traditional MEF methods. For example,Liu et al. [6] proposed a MEF method based on dense scale-invariant feature transform (SIFT), which uses dense SIFT operators to extract local details from source images, suitable for both static and dynamic scenes, and is able to satisfactorily remove ghosting artifacts in dynamic scenes. [PERSON] et al. [7] proposed a multi-exposure fusion algorithm based on feature evaluation, which can adaptively evaluate the exposure weight of the image, and the obtained fusion image has better brightness. Moreover, it can retain some details but details will be lost in areas with large differences in brightness. In recent years, deep learning has attracted the attention of many researchers and has been applied to the field of MEF. [PERSON] et al. [8] took the lead in applying depth to the MEF field. [PERSON] et al. [9] proposed a CNN-based fusion method for fusing images with different exposure levels. However, deep learning-based methods require many multi-exposure images with ground-truth information for training, which is challenging to meet. Using the traditional MEF method, this paper proposes an exposure weight calculation method based on the weighted average adaptive factor, which is based on the Huang method, and then uses a fast local Laplacian filter for local detail enhancement. The main contributions of this paper are as follows:  
* This paper proposes a new exposure weight evaluation scheme based on the weighted average adaptive factor, determined by the local average brightness and the expected brightness. The local average and expected brightness are used as the adjustment parameters of the exposure evaluation function and the exposure weight of the pixel is adaptively evaluated. * A local detail enhancement method for Laplacian pyramid fusion is proposed, which uses the K-means algorithm to divide the input image sequence into over-exposed images (OEI) and low-exposure images (LEI) according to the image brightness histogram distribution, and then uses the fast local Laplacian filter (FLLF) to enhance the darker regions of the OEI and the brighter regions of the LEI. The rest of this paper is organized as follows.