  The fusion of visible light and infrared images has garnered significant
attention in the field of imaging due to its pivotal role in various
applications, including surveillance, remote sensing, and medical imaging.
Therefore, this paper introduces a novel fusion framework using Res2Net
architecture, capturing features across diverse receptive fields and scales for
effective extraction of global and local features. Our methodology is
structured into three fundamental components: the first part involves the
Res2Net-based encoder, followed by the second part, which encompasses the
fusion layer, and finally, the third part, which comprises the decoder. The
encoder based on Res2Net is utilized for extracting multi-scale features from
the input image. Simultaneously, with a single image as input, we introduce a
pioneering training strategy tailored for a Res2Net-based encoder. We further
enhance the fusion process with a novel strategy based on the attention model,
ensuring precise reconstruction by the decoder for the fused image.
Experimental results unequivocally showcase our method's unparalleled fusion
performance, surpassing existing techniques, as evidenced by rigorous
subjective and objective evaluations.
