FUNet: Frequency-Aware and Uncertainty-Guiding Network for Rain-Hazy Image Restoration

Restoring rain-hazy images is vital for intelligent decision-making in autonomous driving and outdoor surveillance systems, which is a challenging ill-posed problem due to the irreversible nature of image degradation. Despite remarkable success achieved through deep learning, current algorithms are primarily evaluated using given kind of images, and the texture details and frequency domain information are insufficiently explored in most approaches, which greatly limits the performance of the model. To alleviate the above challenges, the frequency-aware and uncertainty-guiding network (FUNet) is proposed for rain-hazy image restoration. The FUNet consists of an end-to-end encoder-decoder architecture with the uncertainty-guided feature refinement (UGFR) and the confidence feature feedback module (CFF). First, the UGFR is designed with the uncertainty estimation (UE), uncertainty local global feature extraction module (ULG), and the frequency component decomposition and fusion (FCDF), which learns the abundant intermediate information in detail for clear image restoration. Second, in order to adequately learn rich semantic features, the CFF module is proposed to provide feedback and guidance on the learning process of the decoder. Third, the frequency-based loss function is designed to ensure training stability, which effectively guarantees the spatial and spectral details of images. Experiments on seven synthetic outdoor datasets and the real-world dataset DQA demonstrate the superiority of the proposed model quantitatively and qualitatively.