Reduced Biquaternion Dual-Branch Deraining U-Network via Multi-Attention Mechanism

As a prerequisite for many vision-oriented tasks, image deraining is an effective solution to alleviate performance degradation of these tasks on rainy days. In recent years, the introduction of deep learning has obtained the significant developments in deraining techniques. However, due to the inherent constraints of synthetic datasets and the insufficient robustness of network architecture designs, most existing methods are difficult to fit varied rain patterns and adapt to the transition from synthetic rainy images to real ones, ultimately resulting in unsatisfactory restoration outcomes. To address these issues, we propose a reduced biquaternion dual-branch deraining U-Network (RQ-D2UNet) for better deraining performance, which is the first attempt to apply the reduced biquaternion-valued neural network in the deraining task. The algebraic properties of reduced biquaternion (RQ) can facilitate modeling the rainy artifacts more accurately while preserving the underlying spatial structure of the background image. The comprehensive design scheme of U-shaped architecture and dual-branch structure can extract multi-scale contextual information and fully explore the mixed correlation between rain and rain-free features. Moreover, we also extend the self-attention and convolutional attention mechanisms in the RQ domain, which allow the proposed model to balance both global dependency capture and local feature extraction. Extensive experimental results on various rainy datasets (i.e., rain streak/rain-haze/raindrop/real rain), downstream vision applications (i.e., object detection and segmentation), and similar image restoration tasks (i.e., image desnowing and low-light image enhancement) demonstrate the superiority and versatility of our proposed method.