Infrared and Visible Image Fusion via Style-Based Recalibration and Edge Enhancement
TL;DR
A lightweight end-to-end IVIF network with two complementary refinement modules that 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.
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.