Infrared and Visible Image Fusion via Style-Based Recalibration and Edge Enhancement

Abstract

Infrared and visible image fusion (IVIF) aims to preserve infrared thermal targets and visible structural textures in one informative image. Although recent attention-based methods improve cross-modal interaction, their post-fusion refinement remains limited in two aspects: modality-specific channel statistics are no longer explicitly exposed after feature mixing, and repeated attention-based aggregation can smooth spatial responses and weaken high-frequency visible details. To address these issues, this work proposes a lightweight end-to-end IVIF network with two complementary refinement modules. MSG carries out cross-modal style-based recalibration by making use of the joint mean and standard deviation of the two pre-fusion encoder features, so that first- and second-order pre-fusion modality statistics can guide post-fusion channel selection. DGM carries out edge enhancement by constructing a parameter-free Sobel detail prior from source images and learning only a lightweight residual modulation to perform restoration of high-frequency evidence. With only 80,160 trainable parameters, the proposed method achieves the best or tied-best value on three of seven standard fusion-quality metrics on FMB and four of seven on LLVIP, and ablation results further confirm the complementary effects of MSG and DGM.