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http://dx.doi.org/10.7472/jksii.2018.19.5.43

Single Image Dehazing Based on Depth Map Estimation via Generative Adversarial Networks  

Wang, Yao (Department of Computer Science and Engineering, Hanyang University)
Jeong, Woojin (Department of Computer Science and Engineering, Hanyang University)
Moon, Young Shik (Department of Computer Science and Engineering, Hanyang University)
Publication Information
Journal of Internet Computing and Services / v.19, no.5, 2018 , pp. 43-54 More about this Journal
Abstract
Images taken in haze weather are characteristic of low contrast and poor visibility. The process of reconstructing clear-weather image from a hazy image is called dehazing. The main challenge of image dehazing is to estimate the transmission map or depth map for an input hazy image. In this paper, we propose a single image dehazing method by utilizing the Generative Adversarial Network(GAN) for accurate depth map estimation. The proposed GAN model is trained to learn a nonlinear mapping between the input hazy image and corresponding depth map. With the trained model, first the depth map of the input hazy image is estimated and used to compute the transmission map. Then a guided filter is utilized to preserve the important edge information of the hazy image, thus obtaining a refined transmission map. Finally, the haze-free image is recovered via atmospheric scattering model. Although the proposed GAN model is trained on synthetic indoor images, it can be applied to real hazy images. The experimental results demonstrate that the proposed method achieves superior dehazing results against the state-of-the-art algorithms on both the real hazy images and the synthetic hazy images, in terms of quantitative performance and visual performance.
Keywords
dehaze; depth estimation; generative adversarial networks;
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Times Cited By KSCI : 3  (Citation Analysis)
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