AMLCA: Additive multi-layer convolution-guided cross-attention network for visible and infrared image fusion

Multimodal image fusion is widely used in the processing of multispectral signals, e.g., visible and infrared images, which aims to create an information-rich fused image by combining the complementary information from different wavebands. Current fusion methods face significant challenges in extracting complementary information from sensors while simultaneously preserving local details and global dependencies. To address this challenge, we propose an additive multi-layer convolution-guided cross-attention network (AMLCA) for visible and infrared image fusion, which consists of two sub-modals, i.e., additive cross-attention module (ACAM) and wavelet convolution-guided transformer module (WCGTM). Specifically, the former enhances feature interaction and captures global holistic information by using an additive cross-attention mechanism, while the latter relies on wavelet convolution to guide the transformer, enhancing the preservation of details from both sources and improving the extraction of local detail information. Moreover, we propose a multi-layer fusion strategy that leverages hidden complementary features from various layers. Therefore, AMLCA can effectively extracts complementary information from local details and global dependencies, significantly enhancing overall performance. Extensive experiments and ablation analysis on public datasets demonstrate the superiority and effectiveness of AMLCA. The source code is available at https://github.com/Wangdl2000/AMLCA-code.