QMSANet: A quaternion multi-scale attention network for robust color image denoising

Existing color image denoising methods often fail to adequately capture correlations among RGB channels, leading to structural blurring and the loss of fine details. To overcome this limitation, we propose QMSANet, a Quaternion Multi-Scale Attention Network designed to explicitly model inter-channel correlations (i.e., correlations among RGB channels) throughout the denoising process, thereby enabling stronger noise suppression and more faithful detail reconstruction. Our network is built around three complementary modules: the Quaternion Multi-Scale Sparse Block (QMSB), the Quaternion Stacked Enhancement Block (QSEB), and the Lightweight Quaternion Attention Block (LQAB). These modules form a cohesive processing pipeline. Specifically, the QMSB first extracts sparse multi-scale features, allowing the model to capture contextual information at different granularities. These features are then refined by the QSEB, which enhances deep inter-channel interactions and stabilizes feature propagation to improve representational quality. Finally, the LQAB adapts the refined features through a lightweight attention strategy that selectively highlights the most informative responses with minimal computational overhead. Together, these modules operate sequentially to address key denoising challenges, improving efficiency while reducing incomplete noise removal, detail loss, and edge artifacts. Extensive experiments on standard color image denoising benchmarks show that QMSANet consistently outperforms state-of-the-art models under both synthetic and real-world noise. Moreover, although blind denoisers typically underperform their non-blind counterparts, our blind variant (i.e., QMSANet-B) still surpasses most representative methods.