Nighttime Haze Removal with Fusion Atmospheric Light and Improved Entropy

Abstract

Haze removal is important for the normal work of computer vision system. However, most of the existing image dehazing methods are aimed at daytime haze images. These methods cannot always work well for night haze images since the spatially non-uniform environmental illumination are present at nighttime scenes that can generate glow. This makes nighttime haze removal from single image is an ill-posed problem with challenges. In this paper, we propose a novel algorithm for single nighttime image haze removal. We first remove the glow effects by decomposing the glow image from the nighttime haze image based on a nighttime haze imaging model which can account for spatially non-uniform environmental illumination and the glow effects in the image. Then, we estimate the atmospheric light by combining multiple patch sizes local atmospheric light using multiscale fusion algorithm. Transmission is estimated by maximizing the objective function which is designed by considering the image contrast and color distortion. Finally, haze is removed using the two estimated parameters. Experimental results show that the proposed algorithm can achieve haze-free results while removing the glow effects.