FAD-Net: Frequency Alignment Dual-Branch Network for Hyperspectral Image Super-Resolution

Hyperspectral image super-resolution (HSI SR) aims to recover high-resolution hyperspectral images from low-resolution observations while preserving spatial details and spectral fidelity. Accurate spectral preservation is a key distinction between HSI SR and natural image SR. Recent hybrid methods combining convolutional neural networks (CNNs) and Transformers have substantially improved spatial reconstruction performance, but spectral fidelity remains insufficiently explored. Our frequency perturbation analysis reveals that CNNs and Transformers exhibit complementary frequency-response characteristics. To explicitly exploit this complementarity, we propose a Frequency Alignment Dual-Branch Network (FAD-Net) for HSI SR. Specifically, the Multi-Scale Frequency Refinement Branch (MFRB) restores local high-frequency details through wavelet decomposition, while the Spatial–Spectral Frequency Interaction Branch (S2FIB) models long-range spatial and spectral dependencies via window-based attention. The two branches are fused by a Frequency Alignment Block (FAB). During training, Hyperspectral Frequency Loss and Spectral Angle Mapper loss are further introduced to constrain the reconstructed results toward the ground truth. Experiments on Chikusei, Botswana, and Pavia Center at ×2, ×3, and ×4 scales show that FAD-Net achieves the best Spectral Angle Mapper (SAM) in all nine settings while maintaining competitive spatial performance.