## 1 Introduction
Multiexposure image fusion is a technique that fuses multiple images of different exposures to produce a single well-exposed image. The input multiple exposure image sequence is required in the algorithm proposed by [12]. The algorithm decomposes each input image into a base layer and a detail layer and fuses the information from both layers to generate the final enhanced image. To improve the image distortion, EFF (Exposure Fusion Framework) [13] studies the relationship between two images with different exposure levels, uses illumination estimation to estimate the exposure map, and adjusts each pixel to normal exposure according to the estimated exposure map. BIMEF (Bio-Inspired Multi-Exposure Fusion Framework) [14] is a low-light image enhancement algorithm based on multiexposure fusion, which first simulates the human eye to adjust the exposure to generate a sequence of multiexposure images, and finally, simulates the human brain to fuse the generated exposure images to obtain the final enhanced image. The overall brightness of the image enhanced by the algorithm based on multiexposure fusion is uniform. However, the limitations of MEF are mainly threefold: (1) It requires more LDR (Low Dynamic Range) images (usually more than two) in the exposure stack to capture the entire dynamic range of the scene, resulting in more storage requirements, processing time, and power. (2) The stumbling block to using deep learning in MEF is the lack of sufficient training data and labels that provide the basis for supervised learning. (3) Fusion of multiple input images can produce a better HDR (High Dynamic Range) image, but in the fusion process, it is easy to fuse the well-exposed area of the original image with the multiexposure image sequence again, and there is a risk of overexposure. To alleviate the problem of overexposure, underexposure, and data limitation, in this study, we propose an exposure fusion cross-attention network (EFCANet) for low-light image enhancement, whose algorithm flow is shown in Figure 2. The Exposure Image Generator (EIG), which estimates the exposure factor of each pixel, then adjusts the exposure of each pixel according to the exposure factor to obtain an exposure-corrected image. As far as we know, the YCbCr channel contains luminance component Y and color component Cb and Cr. The image structure details exist in the luminance channel, and the luminance variations are more prominent in the luminance channel than in the chrominance channel. To better maintain the balance between image color and luminance, we convert the original image and the corresponding exposure-corrected image from RGB color space to YCbCr color space. Then, the Cross-Attention Fusion Module (CAFM) is invoked in the image fusion stage. CAFM can effectively avoid fusion artifacts and color inconsistencies in fused images. CAFM consists of two Cross-Attention Fusions (XAF) and one Channel Attention Fusion (CAF). XAF compensates for the low-light image of the primary input with the exposure-corrected image of the secondary input. CAF implements a two-branch fusion to produce better fusion results. The first CAFM reconstructs the first intermediate enhancement result, which is still in YCbCr color space. Four CAFM iterations are used in the network, and the input of the latter CAFM is the output of the previous CAFM and the corresponding exposure-corrected image of the original low-light image. Finally, the YCbCr image is converted to RGB image to obtain the final enhancement result. The main contributions of this paper are summarized as follows:  
* An exposure image generator is designed to reconstruct an exposure-corrected image for any single low-light image. This generator generates exposure-corrected images corresponding to low-illumination images by calculating the exposure factor of each pixel, which effectively improves the global brightness of the image. The exposure-corrected image is used as part of the input for image fusion.