Multi-path dilated convolution network for haze and glow removal in nighttime images

In this paper, we address the single-image haze removal problem in nighttime scenes. The night haze removal is a severely ill-posed problem due to the presence of various visible night light sources with varying colors and non-uniform illumination. These light sources are of different shapes and introduce a noticeable amount of glow in the night scenes. To overcome these effects, we introduce a deep learning-based DeGlow–DeHaze iterative model which accounts for varying colors and glows. The proposed model is a linear combination of three terms: the direct transmission attenuation, airlight and glow. First, a multi-path dilated convolution DeGlow network is introduced to interactively learn the local features with different reception fields and reduce the glow effect. The glow term is estimated by a binary mask that informs the location of the illumination source. As a result, the nighttime image is only left with only direct transmission and airlight terms. Finally, a separate post-processing DeHaze network is included to remove the haze effect from the reduced glow image. For our model training, we collected the night hazy images from internal and external resources, synthesized transmission maps from the NYU depth datasets, and consequently restored the haze-free images. The quantitative and qualitative evaluations show the effectiveness of our model on several real and synthetic images and compare our results with existing night haze models. The experimental results demonstrate that our multi-path CNN model outperforms other state-of-the-art methods in terms of PSNR (19.25 dB), SSIM (0.9958) evaluation parameters and computation time.