A Novel Simulation for Polarization Dehazing

Haze and fog, as severe weather conditions, have absorbing and scattering effects on the optical images, severely affecting image quality. Polarization-based dehazing algorithms can estimate the original radiance distribution of the scene through the polarization of skylight and transmitted light. However, current traditional methods lack consideration for the polarization of transmitted light, and the datasets required for deep learning-based methods are difficult to obtain. This paper proposes a polarized haze image synthesis method that can generate scene intensity and polarization after passing through different distances and concentrations from existing DoFP images, Specially, we equate the attenuation of the scattering medium to a superposition of a series of Mueller matrices, and in combination with the atmospheric attenuation model, which thoroughly integrates both intensity characteristics and polarization properties. We establish a comprehensive polarization dataset for image dehazing, including 300 sets of simulated data, 40 sets real world data from artificial scenes with haze-free ground truth and 40 sets real world data from urban scenes. The network model trained on our simulated dataset demonstrates the effectiveness of the simulation method in testing experiments.