Night Time Haze and Glow Removal using Deep Dilated Convolutional Network
This solves the problem of improving image clarity in nighttime scenes with haze and glow for applications like surveillance or photography, but it is incremental as it builds on existing deep learning approaches.
The paper tackles nighttime single image haze removal by addressing glow and non-uniform illumination, introducing a deep learning DeGlow-DeHaze iterative architecture that outperforms state-of-the-art methods in computation speed and image quality.
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.