LRFE-CL: A self-supervised fusion network for infrared and visible image via low redundancy feature extraction and contrastive learning

Abstract

Aiming to amalgamate the distinct yet complementary attributes inherent to infrared and visible spectra, Infrared and Visible Image Fusion (IVIF) endeavors to preserve an optimal intensity distribution while curtailing the superfluous data within the resultant image. Nonetheless, it is an incontrovertible reality that the majority of extant methods are prone to engendering an undesirable redundancy of features, in addition to a marked degradation throughout the fusion process. To address the issue, we proposed a self-supervised fusion network for infrared and visible image via low redundancy feature extraction and contrastive learning, termed as LRFE-CL. Specifically, the Low Redundancy Feature Extraction Network (SSFN-LRAE) has been developed to effectively extract low-redundancy features from infrared and visible images using the self-supervised strategy and the Channel Shift Attention Module (CSAM). Subsequently, the Contrastive Fusion Decoder (CFDN) is designed to fuse multi-layer features extracted from the SSFN-LRAE. To prevent feature degradation, a novel regularized contrastive loss mechanism targeting multi-layer features has been formulated to optimize our CFDN, ensuring the fusion of comprehensive texture details and brightness information. Our comprehensive experimental results unequivocally demonstrate the exceptional performance of our method, both in terms of visual effects and fusion performance.