MRQE-Net: A Mixture of Reduced Biquaternion Experts Network for General Digital Photography Image Fusion

Digital photography image fusion aims to integrate essential information from multiple source images. However, the current methods predominantly rely on a single-channel fusion strategy, failing to fully leverage color pixel information. Furthermore, the significant variations in fusion mechanisms across tasks limit the performance of generic models. These factors can lead to color shifts and the loss of fine details. To address these issues, we propose a novel approach named MRQE-Net for general digital photography image fusion. The proposed network treats RGB pixels as a unified entity by encapsulating them into reduced biquaternion (RQ), and achieves task-specific fusion through mixture of RQ experts (MoRQE) module within a unified model. Specifically, we first develop the RQ spatial attention (RQSA) and RQ multi-scale pixel attention (RQMSPA) modules to enhance salient features. Then, we propose the deep feature invertible module (DFIM) to efficiently extract the complementary deep information. Finally, to enable customized fusion and feature enhancement for each task, we propose the adaptive feature synthesis amplification module (AFSAM). To the best of our knowledge, this is the first attempt to perform digital photography image fusion in RQ neural networks. Extensive experiments demonstrate that the MRQE-Net outperforms state-of-the-art methods across multiple metrics for each subtask.