Depth estimation for image dehazing of surveillance on education

Foggy weather brings lots of inconvenience for outdoor safety surveillance in the densely populated school education area. Research on image and video dehazing is able to solve this problem. Most existing methods recover the haze-free scenes relying on the atmospheric scattering model in image dehazing, which often suffer from halo artifacts because of the indistinct edges in the scene depth map. L-0 gradient minimization is introduced to better preserve and locate important edges globally to optimize the scene depth map, making use of this physical model in this paper. Firstly, a rough scene depth map based on the inherent boundary constraint prior on the scene is estimated. Secondly, the rough scene depth map in bright regions is compensated with an adaptive term. Then this compensated scene depth map is put into an optimizing framework to get a refined depth map to make it closer to the ideal scene depth. Finally, with the refined depth map and global atmospheric light, we can recover the haze-free scenes using the atmospheric scatting model. Experimental results show the proposed is better to obtain haze-free scenes with sharp edges, abundant details and vivid color while dealing well with bright areas.