  In this paper, we address the single image haze removal problem in a
nighttime scene. The night haze removal is a severely ill-posed problem
especially due to the presence of various visible light sources with varying
colors and non-uniform illumination. These light sources are of different
shapes and introduce noticeable glow in night scenes. To address these effects
we introduce a deep learning based DeGlow-DeHaze iterative architecture which
accounts for varying color illumination and glows. First, our convolution
neural network (CNN) based DeGlow model is able to remove the glow effect
significantly and on top of it a separate DeHaze network is included to remove
the haze effect. For our recurrent network training, the hazy images and the
corresponding transmission maps are synthesized from the NYU depth datasets and
consequently restored a high-quality haze-free image. The experimental results
demonstrate that our hybrid CNN model outperforms other state-of-the-art
methods in terms of computation speed and image quality. We also show the
effectiveness of our model on a number of real images and compare our results
with the existing night haze heuristic models.
