NIGHTTIME HAZE REMOVAL WITH SPATIALLY VARIANT AMBIENT LIGHT AND SALIENCY-WEIGHTED FUSED TRANSMISSION

Different from hazy images captured in the daytime, the ambient illumination of nighttime hazy images is not globally homogeneous. Highlight and lowlight regions have different transmission properties in nighttime hazy scenes. In this paper, we propose a nighttime dehazing model without using the dark channel prior. We estimate the ambient illumination via the Difference of Gaussian (DoG), which selectively retains the high-frequency edges of brightness mutation. We propose a coefficient fusion algorithm in the LAB color space using Homomorphic filtering to estimate the transmission of highlight regions. And we propose a Retinex-like transmission estimation model for lowlight regions. Then we acquire the global saliency-weighted fused transmission. Finally, we get the haze-free results via the nighttime atmospheric scattering model. Experimental results show that our method outperforms other state-of-the-art methods in both color and detail recovery.