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Title |
Single Image Dehazing: An Analysis on Generative Adversarial Network |
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Author |
Amina Khatun, Mohammad Reduanul Haque , Rabeya Basri, and Mohammad Shorif Uddin |
| Citation |
Vol. 24 No. 2 pp. 136-142 |
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Abstract |
Haze is a very common phenomenon that degrades or reduces the visibility. It causes various problems where high quality images are required such as traffic and security monitoring. So haze removal from images receives great attention for clear vision. Due to its huge impact, significant advances have been achieved but the task yet remains a challenging one. Recently, different types of deep generative adversarial networks (GAN) are applied to suppress the noise and improve the dehazing performance. But it is unclear how these algorithms would perform on hazy images acquired “in the wild” and how we could gauge the progress in the field. This paper aims to bridge this gap. We present a comprehensive study and experimental evaluation on diverse GAN models in single image dehazing through benchmark datasets. |
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Keywords |
Image dehazing; deep learning; convolutional neural networks (CNN); generative adversarial networks (GAN). |
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