  In the study, we present AMFusionNet, an innovative approach to infrared and
visible image fusion (IVIF), harnessing the power of multiple kernel sizes and
attention mechanisms. By assimilating thermal details from infrared images with
texture features from visible sources, our method produces images enriched with
comprehensive information. Distinct from prevailing deep learning
methodologies, our model encompasses a fusion mechanism powered by multiple
convolutional kernels, facilitating the robust capture of a wide feature
spectrum. Notably, we incorporate parallel attention mechanisms to emphasize
and retain pivotal target details in the resultant images. Moreover, the
integration of the multi-scale structural similarity (MS-SSIM) loss function
refines network training, optimizing the model for IVIF task. Experimental
results demonstrate that our method outperforms state-of-the-art algorithms in
terms of quality and quantity. The performance metrics on publicly available
datasets also show significant improvement
