WaveFusion: Wave-Aware Feature Mixing Network for Multisensor Fusion of Infrared and Visible Images

Infrared and visible image fusion aims to overcome the limitations of each modality by combining their complementary advantages, thereby enhancing the performance of high-level vision tasks. Many deep learning-based methods generate fused images with rich details and overall consistency by capturing global dependencies among features. However, these methods often neglect cross-modal semantic relationships and fail to capture global dependencies and semantic information in image features, limiting their effectiveness in improving high-level visual task performance. To address these challenges, we propose WaveFusion, a novel progressive fusion network based on the multilayer perceptron (MLP) architecture with global receptive fields. Our method first divides the input image into a predefined number of patches and represents them as wave functions, which are defined by their real values and phases. Subsequently, by employing a pretrained semantic segmentation network and a semantic loss function, WaveFusion effectively integrates semantic information from the image patches into the wave function representation and progressively merges the phases from both modalities. As a result, our method captures semantic information from image patches across various scenes, enabling dynamic adjustments to their fusion weights. Extensive experiments demonstrate that our method surpasses state-of-the-art methods in both visual effects and quantitative metrics. Furthermore, these fused images enhance the performance of the semantic segmentation network, resulting in an approximate 3.5% increase in mean Intersection over Union (mIoU). The source code and pretrained model will be released at https://github.com/zc617/WaveFusion