Towards Accurate Single Image Sand Dust Removal by Utilizing Uncertainty-Aware Neural Network

Bingcai WeiHui Liu^*Chuang QianHaoliang ShenYibiao ChenYixin Wang

• Wuhan University, Wuhan, China

Although deep learning methods have made significant strides in single image sand dust removal, the heterogeneous uncertainty induced by dusty environments poses a considerable challenge. In response, our research presents a novel framework known as the Hierarchical Interactive Uncertainty-aware Network (HIUNet), which first models uncertainty via Bayesian neural networks to extract robust shallow features, then selects valuable features through a frequency mechanism, and finally enhances them via a dedicated module to reconstruct highquality clean images.HIUNet leverages Bayesian neural networks for the extraction of robust shallow features, bolstered by pre-trained encoders for feature extraction and the agility of lightweight decoders for preliminary image reconstitution. Subsequently, a feature frequency selection mechanism is activated to enhance overall performance by strategically identifying and retaining valuable features while effectively suppressing redundant and irrelevant ones. Following this, a feature enhancement module is applied to the preliminary restoration. This intricate fusion culminates in the production of a restored image of superior quality. Our extensive experiments, using our proposed Sand11K dataset that exhibits various levels of degradation from dust and sand, confirm the effectiveness and soundness of our proposed method.