In this paper, we present a Hybrid Spectral Denoising Transformer (HSDT) for hyperspectral image denoising. Challenges in adapting transformer for HSI arise from the capabilities to tackle existing limitations of CNN-based methods in capturing the global and local spatial-spectral correlations while maintaining efficiency and flexibility. To address these issues, we introduce a hybrid approach that combines the advantages of both models with a Spatial-Spectral Separable Convolution (S3Conv), Guided Spectral Self-Attention (GSSA), and Self-Modulated Feed-Forward Network (SM-FFN). Our S3Conv works as a lightweight alternative to 3D convolution, which extracts more spatial-spectral correlated features while keeping the flexibility to tackle HSIs with an arbitrary number of bands. These features are then adaptively processed by GSSA which per-forms 3D self-attention across the spectral bands, guided by a set of learnable queries that encode the spectral signatures. This not only enriches our model with powerful capabilities for identifying global spectral correlations but also maintains linear complexity. Moreover, our SM-FFN proposes the self-modulation that intensifies the activations of more informative regions, which further strengthens the aggregated features. Extensive experiments are conducted on various datasets under both simulated and real-world noise, and it shows that our HSDT significantly outperforms the existing state-of-the-art methods while maintaining low computational overhead. Code is at https: //github.com/Zeqiang-Lai/HSDT.

翻译：本文提出了一种用于高光谱图像去噪的混合光谱去噪Transformer（HSDT）。在将Transformer适配于HSI时，面临的挑战源于其在捕捉全局与局部空间-光谱相关性方面的能力限制，同时需兼顾CNN方法的效率与灵活性。为解决这些问题，我们引入了一种混合方法，该方法结合了两种模型的优势，采用空间-光谱可分离卷积（S3Conv）、引导光谱自注意力（GSSA）和自调制前馈网络（SM-FFN）。S3Conv作为3D卷积的轻量级替代方案，能够提取更多空间-光谱相关特征，同时保持处理任意波段数HSI的灵活性。这些特征随后由GSSA自适应处理，该模块在光谱波段上执行3D自注意力，并由一组编码光谱特征的Learnable Queries引导。这不仅赋予了模型强大的全局光谱相关性识别能力，还保持了线性复杂度。此外，SM-FFN提出了自调制机制，强化了更具信息性区域的激活，进一步增强了聚合特征。我们在模拟和真实噪声下的多种数据集上进行了广泛实验，结果显示，HSDT在保持低计算开销的同时显著优于现有最先进方法。代码地址：https://github.com/Zeqiang-Lai/HSDT。