Cross-Modal Image Fusion via Structure Preservation and Detail Enhancement Optimization

Existing end-to-end infrared-visible fusion methods often blur edges, smooth textures and weaken target-to-background contrast. We therefore propose Structure-Detail Constrained Fusion (SDC-Fusion), a frequency-decoupled framework with separate constraints on structure and detail. The proposed method employs the Haar wavelet transform to decompose the source images into low-frequency structural and high-frequency detail components. The high-frequency branch uses a local directional-energy prior to construct the target guidance. Unlike existing Rectified Flow-based approaches that operate on the full image or a generic latent representation, our gated module applies Rectified Flow only to the high-frequency wavelet subbands. It learns a few-step residual trajectory from the visible high-frequency coefficients to the target representation, enhancing infrared target boundaries and visible textures without altering low-frequency structure. In the low-frequency branch, adaptive weighting, four-directional Mamba scanning, and multi-dilation depthwise convolutions are integrated to preserve global luminance and background structure while optimizing local grayscale transitions. Comparative experiments against eleven representative fusion methods on the MSRS and M³FD datasets show that SDC-Fusion ranks first in SSIM, VIF, Q_abf, SF, and PSNR on MSRS, and first in SSIM, VIF, Q_abf, SD, and PSNR on M³FD, while ranking second in the remaining two metrics on each dataset. Relative to the strongest competing result, the largest improvements reach 10.44% in SF on MSRS and 5.72% in VIF on M³FD. The model contains 0.535 M parameters and requires 67.5 G FLOPs.