QMSANet: A Quaternion Multi-Scale Attention Network for Color Image Denoising
Yi Liu, Qi Xie, Yu Guo, Guoqing Chen, Boying Wu, Deyu Meng, Jean-Michel Morel, Qiyu Jin, Michael Kwok-Po Ng
⚠ This is a preprint. It may change before it is accepted for publication.

Abstract

Color image denoising is a critical task in computer vision, often hindered by the underutilization of inter-channel correlations, resulting in color distortion and loss of fine details. We propose QMSANet, a Quaternion Multi-Scale Attention Network, to address these challenges by leveraging quaternion operations for color image denoising. Operating in the quaternion domain, QMSANet preserves channel dependencies across all processing stages, enhancing noise suppression and detail retention. The network comprises three innovative modules: the Quaternion Multi-Scale Sparse Block (QMSB) for extracting multi-scale features with sparsity enforcement, the Quaternion Stacked Enhancement Block (QSEB) for refining deep features through inter-channel interactions, and the Lightweight Quaternion Attention Block (LQAB) for adaptively focusing on salient features with minimal computational overhead. These modules collectively mitigate color deviation, detail loss, and edge artifacts. Extensive experiments on benchmark datasets demonstrate that QMSANet outperforms state-of-the-art denoising models in both synthetic and real-world noisy conditions. Typically, a blind denoiser exhibits diminished performance in comparison to a non-blind denoiser. However, QMASNet-B, a blind denoiser constructed based on our model, also surpasses most of the comparison models. At sigma = 15, QMASNet and QMASNet-B achieve PSNR improvements of 0.53 dB and 0.50 dB, respectively, compared to the state-of-the-art method on CBSD68. Visual comparisons further highlight its ability to preserve structural details. QMSANet offers a balanced, efficient solution for high-quality color image denoising, with significant potential for real-world applications.

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