Multiscale-Sparse Spatial-Spectral Transformer for Hyperspectral Image Denoising

Improving hyperspectral image (HSI) quality is crucial in subsequent applications. Current transformer-based methods effectively remove mixed noise from the original HSIs. However, there is limited research on targeted modeling of the spatial locality and edge properties of HSI. In addition, the original transformer computes global query-key pairs indifferently, resulting in equal weights for dissimilarity features, noise, and essential information, thus interfering with the restoration of clean images. To address these issues, this study proposes an effective HSI denoising network called multiscale-sparse spatial-spectral transformer (MS3T) to achieve end-to-end mixed noise removal. Specifically, we reconstruct the attention module in the original transformer by adopting a dual-stream approach to selectively explore the 3-D information of HSI from both spatial and spectral perspectives. In the spatial domain, we construct a multiscale context-capturing module based on the local and non-local similarity properties of HSI to establish remote connections from local to global. In the spectral domain, we develop a top-k selection operator to calculate the similarity scores of query-key pairs and select important semantic information for efficient feature aggregation. Both of the above modules alleviate the computational complexity issue of the original transformer to different extents, and improve the mixed noise removal performance of the whole network. To validate the effectiveness and efficiency of MS3T, we conduct synthetic and real experiments on multiple datasets, and the final results demonstrate that our method outperforms the state-of-the-art methods in both metric evaluation and visual effects; the reproducible code is available at
https://github.com/Zilong-Xiao/MS3T
.