Hyperspectral Image Super-Resolution Based on Adapter-Enhanced Foundation Models

Hyperspectral images (HSIs) contain rich spectral information and are highly valuable in applications such as remote sensing monitoring and target identification. However, due to limitations in imaging technology and cost constraints, existing HSIs generally suffer from low spatial resolution. Recently, several hyperspectral vision foundation models have been proposed, employing large-scale network architectures and newly constructed large-scale datasets, achieving remarkable performance in HSI super-resolution (SR) and other hyperspectral vision tasks. However, as these general-purpose foundation models are designed to accommodate diverse vision tasks, their architectures exhibit inherent limitations when applied specifically to SR as a low-level vision task. To address this architectural limitation, we propose HyperAdapter, an enhanced network based on HyperSIGMA that employs task-specific architectural optimization to fully exploit the performance potential of foundation models for SR tasks. Specifically, we introduce a spatial–spectral prior module (SSPM) to improve the model’s ability to capture local spatial context and spectral correlations in HSI, along with an adapter module to effectively integrate and refine the spatial and spectral features of the base model, ensuring better adaptation to the HSI SR task. Experiments on three public HSI SR datasets demonstrate that the proposed method significantly improves reconstruction performance, validating its superiority and practical utility.