Multi-Exposure image Fusion (MEF) aims to combine the complementary information in the over-underexposed source image pairs or sequences to obtain a fused image with rich texture details, appropriate brightness, and pleasure visual quality. Existing multi-exposure image fusion networks predominantly employ Convolutional Neural Networks (CNN), which exhibit limited capacity in capturing global context information. For this reason, we design a network based on spatial-frequency domain aggregate, named SFANet. In general, the network adopts a U-shape structure. Firstly, we extract different scale...
