## 1 Introduction  
Hyperspectral image (HSI) captures continuous spectral bands, resulting in high-dimensional data with dozens to hundreds of channels, which enables precise characterization of subtle land surface features [1, 2]. Compared to traditional grayscale or RGB images, HSI provides a higher density of spectral information and a more detailed representation of spectral features [3]. This unique advantage in spectral resolution makes HSI indispensable in various fields, including environmental monitoring [4, 5], precision agriculture [6], mineral resource exploration [7, 8, 9] and medical image diagnostics [10, 11]. However, due to the physical limitations of sensors and the influence of complex imaging conditions, HSIdata often suffer from mixed noise (such as Gaussian noise, impulse noise, and striping) and other degradation effects [12; 13]. These factors not only degrade image quality but also significantly reduce the accuracy of subsequent hyperspectral interpretation tasks, such as land cover classification [14; 15], target recognition [16; 17], and change detection [18]. As a critical preprocessing step, the effectiveness of HSI denoising directly impacts the reliability of these subsequent tasks [19]. Hyperspectral image denoising methods can primarily be categorized into two types: prior-based methods and deep-learning-based methods [20; 21; 22]. Most prior-based methods denoise HSI denoising by utilizing prior knowledge such as total variation [23], sparse representation [24; 25], and low-rank matrices [26; 27; 28]. [PERSON] et al. [29] proposed a wavelet thresholding denoising method that employs adaptive thresholds in the frequency domain to separate noise. [PERSON] et al. [30] introduced the block-matching 4D filtering (BM4D) method, which extends two-dimensional denoising to three-dimensional space, enhancing the performance of spatial-spectral joint denoising. [PERSON] et al. [31] proposed the super Laplacian regularized unidirectional low-rank tensor recovery model (LLRT), which improves restoration performance for multispectral data by exploiting tensor low-rank properties. However, prior-based methods are often limited in their ability to jointly model complex noise in hyperspectral images, and the handcrafted prior constraints struggle to accommodate the dynamic changes in complex scenarios. In recent years, deep learning has achieved remarkable progress in hyperspectral denoising. [PERSON] et al. [32] were the first to introduce three-dimensional convolutional neural networks (3D-CNNs) for this task, overcoming the performance limitations of traditional methods by joint spatial-spectral joint features. [PERSON] et al. [33] designed the HSID-CNN deep residual network, utilizing residual connections to mitigate the vanishing gradient problem. [PERSON] et al. [34] proposed the QRNN3D model, which incorporates an attention mechanism to enhance noise suppression through dynamic feature selection. [PERSON] et al. [35] developed MemNet, a memory network that strengthens long-range dependency modeling through recursive structures. Recent studies have also explored the integration of Transformer architectures into hyperspectral image denoising. For instance, [PERSON] et al. [36] proposed SpectralFormer, which leverages multi-head attention to model the nonlinear correlations among spectral bands. This approach enhances the global modeling capability of deep learning methods and shows great potential in advancing HSI denoising performance. Although existing methods have made significant progress in hyperspectral image denoising, there are still limitations in the feature transfer process. Most network architectures fail to sufficiently account for the joint representation mechanism of spectral and spatial features, typically combining these features through simple concatenation or addition, without deeply exploring the intrinsic relationships between the two. Recently, [PERSON] et al.