Abstract: Hyperspectral image denoising methods based on traditional deep learning usually have difficulty capturing the long-range correlation of spatial positions and the similarity of global irregular local blocks,resulting in loss of detailed information and insufficient structural integrity after denoising.To this end,this paper proposes a new superpixel-level graph feature learning method for hyperspectral image denoising,which aims to use graph neural networks to extract spatial-spectral features and capture the long-range correlation of spatial positions of irregular local blocks to preserve texture details and structural information.Firstly,a gated attention module is designed to suppress noise and enhance spectral correlation,laying the foundation for subsequent superpixel segmentation.Then,a superpixel-level graph aggregation module is designed,which effectively maintains the structural integrity and clarity of internal details of the hyperspectral image by segmenting the hyperspectral image into multiple spatially connected superpixels according to the spatial dimensions and using a shared linear layer to learn the weighted values of pixels in the superpixel.Then,graph convolution is used to update the node information.Finally,nuclear norm regularization is introduced in the training process for constraint by taking advantage of the low rank of hyperspectral images,and a low-rank-spatial spectral denoising loss is proposed to focus on preserving structural information.Experimental results show that the proposed method outperforms the current advanced methods in performance.

Key words: Hyperspectral image denoising, Graph neural networks, Deep learning, Spatial-spectral features, Nuclear norm