Res2NetFuse: A Novel Res2Net-based Fusion Method for Infrared and Visible Images
This work addresses image fusion for applications like surveillance and medical imaging, but it appears incremental as it builds on existing Res2Net architecture with specific enhancements.
The paper tackled the problem of fusing infrared and visible images by introducing a Res2Net-based framework with a novel training strategy and attention model, achieving superior fusion performance compared to existing techniques as shown in subjective and objective evaluations.
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.