MEF-CAAN: Multi-Exposure Image Fusion Based on a Low-Resolution Context Aggregation Attention Network

Recently, deep learning-based multi-exposure image fusion methods have been widely explored due to their high efficiency and adaptability. However, most existing multi-exposure image fusion methods have insufficient feature extraction ability for recovering information and details in extremely exposed areas. In order to solve this problem, we propose a multi-exposure image fusion method based on a low-resolution context aggregation attention network (MEF-CAAN). First, we feed the low-resolution version of the input images to CAAN to predict their low-resolution weight maps. Then, the high-resolution weight maps are generated by guided filtering for upsampling (GFU). Finally, the high-resolution fused image is generated by a weighted summation operation. Our proposed network is unsupervised and adaptively adjusts the weights of channels to achieve better feature extraction. Experimental results show that our method outperforms existing state-of-the-art methods by both quantitative and qualitative evaluation.