Robust Single Image Sand Removal by Leveraging Uncertainty-aware SAM Priors and Prompt Learning with Refined Perceptual Loss

Bingcai Wei, Hui Liu, Chuang Qian, Zijian Li, Wangyu Wu, Zijie Meng

Abstract: Sand dust weather has adverse effects on image quality, making single-image sand dust removal a classic research topic in the field of image restoration. However, existing learning-based image restoration methods fail to account for uncertainties in both data and model dimensions, thus being unable to produce satisfactory results for sand dust image restoration. To address this challenge, we introduce a novel framework called the Uncertainty-aware SAM-aided Prompt-interaction Network (USPNet). USPNet comprises two key modules: the Uncertainty-aware SAM Priors Module (USPM), which addresses data-wise stochastic uncertainties, and the Uncertainty-aware Prompt Learning Module (UPLM), which tackles model-wise epistemic uncertainties. By integrating data-wise and model-wise uncertainty learning, USPNet leverages uncertainty modeling through SAM semantic priors and distributionally representative prompts. Recognizing the unexplored uncertainties inherent in the learning process, we propose an Uncertainty-aware Perceptual Loss (UPL) to enhance the visual quality of restored images through perceptual learning. Through comprehensive perceptual study and analysis of real sand-dust images, we propose a dataset named SanddustClearity. SanddustClearity includes daytime, nighttime synthetic, and real-world sand dust images. Our extensive experiments, conducted on both synthetic and real-world images exhibiting various levels of sand dust degradation, confirm the effectiveness and robustness of our proposed method.