\section{Conclusion}
\label{sec:conclusion}
%In this paper, we propose a dual-domain learning Network (DDLN) for multimodal image fusion that effectively integrates spatial and frequency domain information to generate high-resolution hyperspectral images. By designing a dual-branch fusion module capturing spatial structures and frequency components, including phase and amplitude cues, the proposed method fully leverages the complementary characteristics of different modalities. Extensive experiments on two publicly available datasets demonstrate that DDLN outperforms seven state-of-the-art methods. In future work, we aim to explore adaptive frequency decomposition and extend the framework to broader multimodal tasks such as cross-domain retrieval and remote sensing scene understanding.

In this paper, we propose a Dual-Domain Learning Network (DDLN) for multimodal image fusion, which effectively integrates spatial and frequency domain information to reconstruct high-resolution hyperspectral images. Specifically, we design a dual-branch fusion module that simultaneously captures spatial textures and frequency components—including both phase and amplitude representations—allowing the network to fully exploit the complementary information inherent in different modalities. Extensive experiments conducted on two publicly available benchmark datasets demonstrate that DDLN consistently outperforms seven state-of-the-art fusion-based hyperspectral super-resolution methods across multiple quantitative metrics and visual evaluations. \par
%In future work, we plan to explore adaptive frequency decomposition strategies to further improve representation flexibility, and to generalize the proposed framework to a wider range of multimodal applications such as cross-domain retrieval, semantic segmentation, and remote sensing scene understanding.
In future work, we plan to explore adaptive frequency decomposition strategies to further enhance the flexibility and effectiveness of feature representation. Unlike fixed decomposition methods, adaptive strategies can dynamically adjust the frequency components based on the characteristics of the input data, thereby enabling the model to better capture both low-level and high-level semantic features. This may lead to improved generalization across diverse data distributions and more robust performance in complex real-world scenarios.