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In this paper, we address the single image haze removal problem in anighttime scene. The night haze removal is a severely ill-posed problemespecially due to the presence of various visible light sources with varyingcolors and non-uniform illumination. These light sources are of differentshapes and introduce noticeable glow in night scenes. To address these effectswe introduce a deep learning based DeGlow-DeHaze iterative architecture whichaccounts for varying color illumination and glows. First, our convolutionneural network (CNN) based DeGlow model is able to remove the glow effectsignificantly and on top of it a separate DeHaze network is included to removethe haze effect. For our recurrent network training, the hazy images and thecorresponding transmission maps are synthesized from the NYU depth datasets andconsequently restored a high-quality haze-free image. The experimental resultsdemonstrate that our hybrid CNN model outperforms other state-of-the-artmethods in terms of computation speed and image quality. We also show theeffectiveness of our model on a number of real images and compare our resultswith the existing night haze heuristic models.

Night Time Haze and Glow Removal using Deep Dilated Convolutional Network