Spatial-Spectral Oriented Triple Attention Network for Hyperspectral Image Denoising

Hyperspectral images (HSIs) often suffer from degradation caused by mixed noise, leading to a decline in the performance of subsequent advanced applications. To eliminate noise and improve image quality, transformer-based approaches have been successfully employed. Nevertheless, these strategies often involve large-scale modeling and tedious layer normalization, which causes inefficiencies during the denoising process. Additionally, the neglect of local spectral correlations in HSIs damages the physical properties in recovery, resulting in poor generalization and inefficient denoising performance. To address these problems, we propose an efficient spatial-spectral oriented triple attention network, dubbed S2OTAN, for HSI denoising. Specifically, to fully exploit the physical properties of HSIs, we impose spatial and spectral multiscale hybrid attention in the single-transformer block side-by-side to fuse spatial-spectral information in a parallel manner. For spatial feature extraction, we introduce hybrid spatial attention by constructing attention maps for pixels within and across windows to exploit the local and global similarity in spatial and improve computational efficiency. For spectral feature exploration, we utilize spectral partitioning operations to enhance the adjacent spectral dependences of HSIs and capture contextual information related to correlations. Consequently, our method exhibits a robust feature representation capability for removing mixed noise in HSIs. Extensive experiments on synthetic and real-world noisy scenarios demonstrate that the proposed approach outperforms other state-of-the-art approaches among quantitative metrics and visual effects. For the sake of reproducibility, the code is available at: https://github.com/Zilong-Xiao/S2OTAN .