Work

Joint Modeling of Pixel-Wise Visibility and Fog Structure for Real-World Scene Understanding

Year: 2025

Type: article

Abstract: Reduced visibility caused by foggy weather has a significant impact on transportation systems and driving safety, leading to increased accident risks and decreased operational efficiency. Traditional methods rely on expensive physical instruments, limiting their scalability. To address this challenge in a cost-effective manner, we propose a two-stage network for visibility estimation from stereo image inputs. The first stage computes scene depth via stereo matching, while the second stage fuses depth and texture information to estimate metric-scale visibility. Our method produces pixel-wise visibility maps through a physically constrained, progressive supervision strategy, providing rich spatial visibility distributions beyond a single global value. Moreover, it enables the detection of patchy fog, allowing a more comprehensive understanding of complex atmospheric conditions. To facilitate training and evaluation, we propose an automatic fog-aware data generation pipeline that incorporates both synthetically rendered foggy images and real-world captures. Furthermore, we construct a large-scale dataset encompassing diverse scenarios. Extensive experiments demonstrate that our method achieves state-of-the-art performance in both visibility estimation and patchy fog detection.

Source: Atmosphere

DOI: 10.3390/atmos16101161

Authors: Jiayu Wu, Jiaheng Li, Jianqiang Wang, Xuezhe Xu, Sidan Du, Yang Li

Institution: Nanjing University

Language: English

Topic: Image Enhancement Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,896