基于多尺度协调卷积与自适应加权的红外与可见光图像融合OA

To address the limitations of convolution neural networks-based image fusion models,such as restricted glob-al information perception,high-frequency detail preservation,and the loss function weights configuration,this article proposes a convolution and multilayer perceptron-integrated multiscale coordinate network(CM-MCNet)for high-qual-ity infrared and visible image fusion.In the encoder of CM-McNet,a convolutional weighted permute multilayer per-ceptron module is introduced to enhance spatial understanding by simulating feature permutation and integrates an ad-aptive feature reweighting mechanism to effectively capture global information.Meanwhile,a multiscale coordinate convolution(MsCConv)module is designed,leveraging the advantages of central difference convolution to enhance the retention and expression of high-frequency details.By incorporating multiscale parallel sub-networks,MsCConv en-sures the comprehensive preservation of multi-level features.Moreover,the embedded coordinate attention mechanism jointly modulates channel and spatial dimensions,enhancing complementary information while suppressing redundancy.Furthermore,a data-driven adaptive loss weighting strategy is proposed,which can dynamically adjust the contribution of supervision signals based on image feature statistics.This reduces the complexity of hyperparameter tuning while en-suring the loss function more accurately reflects the characteristics of the source images.Experimental results on the RoadScene,TNO,and M3FD public datasets demonstrate that CM-MCNet generates fused images with sharper edge preservation and more natural texture transitions.Additionally,our method achieves superior performance across vari-ous objective metrics,including information entropy,standard deviation,spatial frequency,visual information fidelity,and average gradient,outperforming existing state-of-the-art fusion methods.This work provides a novel perspective for infrared and visible image fusion and lays a solid foundation for further advancements in the field.