Neural network-based framework for wide visibility dehazing with synthetic benchmarks

Hazy images caused by atmospheric scattering significantly degrade the visibility and performance of computer vision systems, especially in long-range applications. Existing synthetic haze datasets are usually limited to short visibility ranges and fail to adequately model wavelength-dependent scattering effects, leading to suboptimal evaluation of dehazing algorithms. In this study, we propose a physically motivated synthesis method that combines the atmospheric scattering model with channel-specific extinction coefficients for the RGB channels and depth information ranging from 0 to 10 km. This approach enables the construction of the Wide Visibility Synthetic Haze (WVSH) dataset, which spans visibility distances from 50 m to 2 km. Based on WVSH, we design WVDehazeNet, a convolutional neural network that effectively leverages multi-scale spatial features and wavelength-dependent haze priors. Extensive experiments on both WVSH and real-world hazy images demonstrate that WVDehazeNet achieves competitive or superior performance compared with eight state-of-the-art methods in both quantitative and qualitative evaluations. The WVSH dataset and WVDehazeNet provide valuable benchmarks and references for long-range image dehazing research, helping to advance the field.