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Nighttime image dehazing with local atmospheric light and weighted entropy

In this paper, we propose a novel framework for nighttime image dehazing based on a nighttime haze model which accounts for varying light sources and their glow. First, glow effects are decomposed using relative smoothness. Atmospheric light is then estimated by combining global and local atmospheric lights using a local atmospheric selection map. The transmission is estimated by maximizing an objective function designed with weighted entropy. Finally, haze is removed using two estimated parameters which are atmospheric light and transmission. Experimental results validate the proposed method can achieve haze-free results while alleviating the glow effect.