Multiscale Spatio-Temporal Fusion Network for video dehazing

Abstract

Video dehazing aims to restore high-resolution and high-contrast haze-free frames, which is crucial in engineering applications such as intelligent traffic monitoring systems. These monitoring systems heavily rely on clear visual information to ensure accurate decision-making and reliable operation. However, despite significant advances achieved by deep learning methods, they still face challenges when dealing with diverse real-world scenarios. To address these issues, we propose a Multi-Scale Spatio-Temporal Fusion Network (MSTF-Net), a novel framework designed to enhance video dehazing performance in complex engineering environments. Specifically, the MainAux Encoder integrates multi-source information through a progressively enhanced feature fusion mechanism, improving the representation of both global dynamics and local details. Furthermore, the Spatio-Temporal Adaptive Fusion (STAF) module ensures robust temporal consistency and spatial clarity by leveraging multi-level spatio-temporal information fusion. To evaluate our framework, we constructed a challenging dataset named “DarkRoad”, which includes low-light, uneven lighting, and dynamic outdoor scenarios, addressing the key limitations of existing datasets in video dehazing tasks. Extensive experiments demonstrate that MSTF-Net achieves state-of-the-art performance, excelling particularly in applications requiring high clarity, strong contrast, and detailed preservation, providing a reliable solution to video dehazing problems in practical engineering scenarios.