Quaternion Wavelet-Driven Multi-Scale Feature Interaction Network for Color Image Denoising

Real-valued wavelets have achieved great success in image denoising due to their sparse representation capability under multi-scale analysis. However, existing real-valued wavelets suffer from limited directional selectivity and translation sensitivity, which can lead to color distortion and loss of phase information. The quaternion wavelet transform (QWT) offers a new solution by extending each pair of complex filters in the dual-tree complex wavelet transform to quaternion-valued filter banks, generating quaternion high frequency subbands in three principal directions while retaining a low frequency approximation, thus achieving cross channel translation invariance and phase consistency. Based on this, we propose a QWT-driven multi-scale feature interaction network (QMFINet). QMFINet leverages QWT to extract cross channel structured phase features at the same spatial locations, precisely linking color and texture details; it further employs a three-path feature extraction module (TPFEM) to capture multi-scale representations. To effectively fuse features at different resolutions, we design a quaternion ordered channel attention subnet (QOCAS). Experimental results demonstrate that QMFINet outperforms several state-of-the-art color image denoising methods across a range of noise levels, and achieves the best performance at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma =75$</tex-math></inline-formula>, with an average PSNR improvement of approximately 0.3-0.4 dB over the previous state-of-the-art method.