  The deep learning model Transformer has achieved remarkable success in the
hyperspectral image (HSI) restoration tasks by leveraging Spectral and Spatial
Self-Attention (SA) mechanisms. However, applying these designs to remote
sensing (RS) HSI restoration tasks, which involve far more spectrums than
typical HSI (e.g., ICVL dataset with 31 bands), presents challenges due to the
enormous computational complexity of using Spectral and Spatial SA mechanisms.
To address this problem, we proposed Hyper-Restormer, a lightweight and
effective Transformer-based architecture for RS HSI restoration. First, we
introduce a novel Lightweight Spectral-Spatial (LSS) Transformer Block that
utilizes both Spectral and Spatial SA to capture long-range dependencies of
input features map. Additionally, we employ a novel Lightweight
Locally-enhanced Feed-Forward Network (LLFF) to further enhance local context
information. Then, LSS Transformer Blocks construct a Single-stage Lightweight
Spectral-Spatial Transformer (SLSST) that cleverly utilizes the low-rank
property of RS HSI to decompose the feature maps into basis and abundance
components, enabling Spectral and Spatial SA with low computational cost.
Finally, the proposed Hyper-Restormer cascades several SLSSTs in a stepwise
manner to progressively enhance the quality of RS HSI restoration from coarse
to fine. Extensive experiments were conducted on various RS HSI restoration
tasks, including denoising, inpainting, and super-resolution, demonstrating
that the proposed Hyper-Restormer outperforms other state-of-the-art methods.
