To address the issue of insufficient extraction and fusion of complementary information in infrared and visible image fusion, this study proposes a pulse-coupled dual adversarial learning network. The network utilizes dual discriminators that target infrared objects and visible texture details in the fused images, with the goal of preserving and enhancing modality-specific features. We also introduce a pulse-coupled neural network featuring a combined learning mechanism to effectively extract salient features and detailed information from the images. During the fusion stage, we implement a cross-modality fusion module guided by cross-attention, which further optimizes the complementary information between modalities and minimizes redundant features. We conducted comparative qualitative and quantitative analyses against nine representative fusion methods in the TNO, M³FD, and RoadScene datasets. Results show that the proposed method demonstrates superior performance in evaluation metrics, such as mutual information and sum of correlation differences. The method produces fused images with high contrast and rich detail and achieves better results in target detection tasks.