Two-Exposure Image Fusion Based on Cross Attention Fusion.

High Dynamic Range (HDR) imaging requires the fusion of images captured with multiple exposure ratios in the same scene to cover the entire dynamic range. With only a few low dynamic range (LDR) images, it remains a challenging task. The paper presents a novel two-exposure image fusion model that features the proposed Cross Attention Fusion Module (CAFM) to use one image's highlight to compensate for the other's content loss caused by under-exposure or over-exposure. The CAFM consists of Cross Attention Fusion and Channel Attention Fusion to achieve a dual-branch fusion for producing superior fusion results. The extensive experimental results on benchmark HDR public datasets demonstrate that the proposed model performs favorably against the state-of-the-art image fusion methods.