LAUSR

Polarization-based dehazing methods can enhance the quality of haze images. However, existing methods tend to a manual selection of sky area and bias coefficient to estimate the degree of polarization (DoP) of the airlight, which leads to inaccurate estimation of the airlight. Aiming at the problem, a reconstruction algorithm based on the blind separation model of polarized orthogonal airlight is proposed. Importantly, the depth-dependent DoP of the airlight is automatically estimated without manual selection of sky area and bias coefficient. To reduce the interference of white objects on the estimation of airlight at infinity, an adaptive estimation method using the deviation between the DoP of the airlight and incident light is proposed. In order to accurate estimate the airlight from the airlight at infinity, a blind separation model of the airlight with multi-regularization constraints is established based on the decomposition of the airlight at infinity into a pair of polarized components with orthogonal angles. The experimental results show that the method effectively improves the visibility of scenes under different haze concentrations, especially in dense or heavy haze weather.