To address the requirement for efficient registration and fusion of unregistered infrared and visible images, we propose RRFNet-DCDAN, a unified infrared?visible image registration and fusion network incorporating a dynamic convolutional dual attention mechanism. This model enables adaptive fusion of unregistered multimodal images with different resolutions. RRFNet-DCDAN combines a dual attention network (DAN) and dynamic convolution attention (DCA) module. The DAN is employed to emphasize the salient features of infrared targets and preserve the texture details of visible images. Simultaneously, the DCA efficiently handles input image pairs with different resolutions. Based on the MSRS and RoadScene datasets, the proposed method outperforms other benchmark models (e.g., GTF and DenseFuse) in five evaluation metrics (e.g., mutual information and visual fidelity). Meanwhile, the image processing time is reduced by 20.57%. The proposed method provides a promising way for processing images with different resolutions.