<p>Evaluation metrics used in this paper.</p>

Traditional multi-exposure fusion (MEF) methods often struggle to simultaneously preserve local and global features as well as chrominance information. To address this issue, we propose MEF-TBN, a three-branch network consisting of a context aggregation attention network (CAAN) branch for local feature extraction, a transformer branch for global dependency modeling, and a color enhancement branch for natural color restoration.

Specifically, the CAAN and transformer branches generate low-resolution weight maps based on local and global features of luminance components, and a merging module is proposed to refine and enhance the local and global features with several merger blocks, and then estimate high-resolution weights by adopting the guided filtering for upsampling operation. Meanwhile, the color enhancement branch is capable of learning the inherent correlation between luminance and chrominance components.