Dynamic Recurrent Self-Refinement Network for Hyperspectral Remote Sensing Image Super-Resolution

Hyperspectral image super-resolution (HSISR) is crucial for enhancing the spatial detail of hyperspectral images (HSIs). However, existing single HSI super-resolution (SR) methods face challenges with effective use of spectral context and generalization across varying spectral bands. We propose a dynamic recurrent self-refinement network (DRSN) to address these limitations. DRSN innovatively models the HSI SR process as a non-linear dynamic system evolving along the spectral dimension, where the spectral groups are treated as state variables. This formulation inherently allows DRSN to process HSIs with arbitrary band counts, thereby broadening the application scope of HSISR. The key contributions in DRSN can be summarized into two novel modules: adaptive state prediction-updating (ASPU) and bidirectional cross-state alignment (BCSA). The ASPU employs an uncertainty-guided adaptive activation mechanism to dynamically refine the current state by selectively integrating inter-state complementary information while suppressing irrelevant context. The BCSA utilizes an efficient back-and-forth caching strategy and masked inter&intra-state attention to mitigate information imbalance and achieve effective bidirectional contextual alignment without substantial computational overhead. Experiments on benchmark HSI datasets demonstrate that DRSN achieves state-of-the-art performance, exhibits wide applicability, and maintains a lightweight and computationally efficient structure. The source code is available at https://github.com/hgpftd/DRSN.