PSFD-Net: Progressive Supervision-Based Frequency-Domain Decoupling Network for Hyperspectral and Multispectral Image Fusion

Hyperspectralmultispectral image fusion (HMIF) aims to reconstruct high-fidelity, high spatial-resolution hyperspectral images (HR-HSI) by combining the complementary features of both modalities. However, the low spatial resolution of low-resolution hyperspectral images (LR-HSI) and the sparse spectral sampling of high-resolution multispectral images (HR-MSI) limit the representation of complementary features. Spatial-domain methods still suffer from spatial and spectral distortions, which stem from the coupling between spatial and spectral features in the spatial domain, making it difficult to decouple and calibrate complementary features and ultimately affecting fusion accuracy. To address this issue, we tackle the feature coupling problem from a frequency-domain perspective. Specifically, we propose a Progressive Supervision-Based Frequency Domain Decoupling Network (PSFD-Net). The core idea of PSFD-Net is to decouple, calibrate, and effectively fuse high- and low-frequency components in the frequency domain, while imposing training constraints to facilitate high-fidelity fusion reconstruction. We first introduce an Adaptive Frequency Decoupling (AFDM) Module to decompose features into high- and low-frequency components, and then design a Physically-Aware Feature Calibration (PAFC) Module to calibrate and enhance frequency components under physically-aware constraints. Finally, a Full-frequency Guided SpatialSpectral Fusion (FGSS) Module is employed to effectively integrate frequency information under full-frequency guidance. In addition, we design a progressive supervision strategy based on an annealing mechanism to achieve high-fidelity reconstruction from global spectral information to local structural details. Extensive experiments demonstrate that the proposed PSFD-Net outperforms current state-of-the-art methods.