S2CANet: A self-supervised infrared and visible image fusion based on co-attention network

Abstract

Existing methods for infrared and visible image fusion (IVIF) often overlook the analysis of common and distinct features among source images. Consequently, this study develops A self-supervised infrared and visible image fusion based on co-attention network, incorporating auxiliary networks and backbone networks in its design. The primary concept is to transform both common and distinct features into common features and reconstructed features, subsequently deriving the distinct features through their subtraction. To enhance the similarity of common features, we designed the fusion block based on co-attention (FBC) module specifically for this purpose, capturing common features through co-attention. Moreover, fine-tuning the auxiliary network enhances the image reconstruction effectiveness of the backbone network. It is noteworthy that the auxiliary network is exclusively employed during training to guide the self-supervised completion of IVIF by the backbone network. Additionally, we introduce a novel estimate for weighted fidelity loss to guide the fused image in preserving more brightness from the source image. Experiments conducted on diverse benchmark datasets demonstrate the superior performance of our S2CANet over state-of-the-art IVIF methods.