Nighttime Haze Removal with Glow Decomposition Using GAN
| Autor: | Gyeonghwan Kim, Beomhyuk Koo |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Daytime
Haze business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Convolutional neural network GeneralLiterature_MISCELLANEOUS Transmission (telecommunications) 0202 electrical engineering electronic engineering information engineering Environmental science 020201 artificial intelligence & image processing Artificial intelligence Single image business Generative adversarial network ComputingMethodologies_COMPUTERGRAPHICS Remote sensing |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030414030 ACPR (1) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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