Non-local feature aggregation quaternion network for single image deraining

The existing deraining methods are based on convolutional neural networks (CNN) learning the mapping relationship between rainy and clean images. However, the real-valued CNN processes the color images as three independent channels separately, which fails to fully leverage color information. Additionally, sliding-window-based neural networks cannot effectively model the non-local characteristics of an image. In this work, we proposed a non-local feature aggregation quaternion network (NLAQNet), which is composed of two concurrent sub-networks: the Quaternion Local Detail Repair Network (QLDRNet) and the Multi-Level Feature Aggregation Network (MLFANet). Furthermore, in the subnetwork of QLDRNet, the Local Detail Repair Block (LDRB) is proposed to repair the backdrop of an image that has not been damaged by rain streaks. Finally, within the MLFANet subnetwork, we have introduced two specialized blocks, namely the Non-Local Feature Aggregation Block (NLAB) and the Feature Aggregation Block (Mix), specifically designed to address the restoration of rain-streak-damaged image backgrounds. Extensive experiments demonstrate that the proposed network delivers strong performance in both qualitative and quantitative evaluations on existing datasets. The code is available at https://github.com/xionggonghe/NLAQNet .