Color Image Denoising Using Reduced Biquaternion U-Network

The reduced biquaternion-valued neural network (RQV-CNN) has recently seen tremendous success for color image processing. However, existing RQV-CNNs are relatively simple in structure and lack effective components, limiting the potential to enhance their performance. Furthermore, an effective method is needed to improve the unsatisfactory denoising results of RQV-CNNs in hard scenes. Therefore, we conduct an in-depth study in the RQ domain and propose a new denoising method, namely RQUNet. To our best knowledge, our approach is the first attempt to construct RQ deconvolutional layer. Building upon this, we construct a deeper RQ network. Additionally, we propose a parallel reduced biquaternion dual attention module to enhance the denoising performance of RQ network in hard scenes. RQUNet is entirely composed of reduced biquaternion-valued blocks, which achieve a significant reduction in the number of parameters. Extensive color image denoising experiments on three different denoising datasets demonstrate that our model achieved the highest average PSNR of 30.66 dB, which surpassed the previous state-of-the-art method by 0.28 dB with less computational cost, and obtained better visualization results.