<p>Multi-Exposure image Fusion is a fundamental task in computer vision. While mainstream deep learning methods have made some progress, they are primarily limited to processing exposure sequences with a fixed number of shots, which restricts their ability to capture scene information and makes it difficult to meet the high dynamic range requirements of real-world scenarios. Therefore, this paper proposes SEGNet, which includes three core components: a pair of pseudo-siamese Spatial-Frequency Attention Modules that correct features based on image correlations by combining spatial and frequency domain attention mechanisms to achieve effective feature complementation; an Exposure-Guided-Merging Module that adaptively weights image features according to the exposure values of the input images, enabling flexible adaptation to different exposure shots; and an Exposure Restoration Module that employs a Vision State-Space Module combined with convolutional modules to balance global restoration and local enhancement, achieving natural transitions in texture, color, and illumination. Additionally, a structural tensor regularization term is innovatively introduced into the loss function to further preserve image details. Extensive qualitative and quantitative experiments demonstrate that SEGNet outperforms existing mainstream models.</p>