Hyperspectral Image Denoising via Quasi-Recursive Spectral Attention and Cross-Layer Feature Fusion.

Hyperspectral images (HSIs) contain rich spatial-spectral information but are highly susceptible to various types of noise during the imaging process, which significantly degrades image quality and undermines the reliability of subsequent applications. To address this issue, we propose a novel end-to-end denoising framework, termed Quasi-Recursive Spectral Attention Network (QRSAN), which aims to design a feature extraction module that leverages the intrinsic characteristics of hyperspectral noise while preserving high-quality spatial and spectral information. Specifically, QRSAN introduces a Quasi-Recursive Attention Unit (QRAU) to jointly model inherent spatial-spectral dependencies, where 2D convolutions are employed for spatial feature extraction and frequency pooling is utilized for spectral representation. In addition, we develop a multi-head spectral attention mechanism to effectively capture inter-band correlations and suppress spectrally dependent noise. To further preserve fine-grained spatial structures and spectral fidelity, we design an adaptive cross-layer skip connection strategy that integrates channel-wise concatenation and transition blocks, enabling efficient feature propagation and fusion within an asymmetric encoder-decoder architecture. Extensive experiments on both synthetic and real HSI datasets demonstrate that QRSAN consistently outperforms existing methods in terms of visual quality and objective evaluation metrics, achieving superior denoising performance and generalization ability while maintaining high spatial-spectral fidelity.