关键词: 高光谱图像去噪, Transformer, 注意力机制, 多尺度特征融合, 空间-光谱建模

Abstract: To address the insufficient exploitation of spectral priors, inadequate modeling of spectral variations at different scales, and limited spatial detail recovery in existing hyperspectral image denoising methods, a hyperspectral image denoising method integrating spectral prior and multiscale feature enhancement was proposed. A hierarchical encoder-decoder architecture was adopted to extract multiscale spatial-spectral features. A distance prior among spectral-related latent feature dimensions was introduced into the attention computation to adaptively calibrate spectral feature interactions, thereby enhancing the modeling of spectral continuity structure and long-range correlations. Meanwhile, multiscale spectral convolution, adaptive spatial gating, and high-frequency detail enhancement were combined to recover local spectral variations, edge textures, and spatial structures. Furthermore, a joint loss function consisting of Charbonnier reconstruction loss, spectral consistency loss, and spatial gradient loss was constructed to constrain pixel-level reconstruction accuracy, spectral directional consistency, and spatial structure preservation capability. On the Harvard dataset, the proposed method achieved PSNR values of 40.62, 38.41, and 36.90 dB under Gaussian noise with standard deviations of 30, 50, and 70, respectively, outperforming the best corresponding comparison model Restormer by 1.27, 1.22, and 1.27 dB. Under Poisson-Gaussian mixed noise, the PSNR reached 42.66 dB, compared with SSIT, an improvement of 0.88 dB. On the Realistic Data, the PSNR, SSIM, SAM, and SPR reached 30.74 dB, 0.9227, 2.49°, and 0.9982, respectively. In the ablation experiment, the complete model improved the PSNR by 0.61 dB and reduced the SAM by 1.47° compared with the baseline model. The proposed model reduces pixel reconstruction errors and spectral deviations under different noise conditions while improving the preservation of edge textures and spatial structures.

Key words: hyperspectral image denoising, transformer, attention mechanism, multiscale feature fusion, spatial-spectral modeling