Nighttime Haze Removal with Glow Decomposition Using GAN

초록

In this paper, we investigate the problem of a single image haze removal in the nighttime. Glow effect is inherently existing in nighttime scenes due to multiple light sources with various colors and prominent glows. As the glow obscures the color and the shape of objects nearby light sources, it is important to handle the glow effect in the nighttime haze image. Even the convolutional neural network has brought impressive improvements for daytime haze removal, it has been hard to train the network in supervised manner in the nighttime because of difficulty in collecting training samples. Towards this end, we propose a nighttime haze removal algorithm with a glow decomposition network as a learning-based layer separation technique. Once the generative adversarial network removes the glow effect from the input image, the atmospheric light and the transmission map are obtained, then eventually the haze-free image. To verify the effectiveness of the proposed method, experiments are conducted on both real and synthesized nighttime haze images. The experiment results show that our proposed method produces haze-removed images that have better quality and less artifacts than ones from previous studies. © Springer Nature Switzerland AG 2020.

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