• Journal of the Korea Computer Graphics Society
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• v.25 no.5
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• pp.1-9
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• 2019

In this paper, we propose a video enhancement method using generative adversarial networks to remove raindrops and restore the background information on the removed region in the coastal wave video imagery distorted by raindrops during rainfall. Two experimental models are implemented: Pix2Pix network widely used for image-to-image translation and Attentive GAN, which is currently performing well for raindrop removal on a single images. The models are trained with a public dataset of paired natural images with and without raindrops and the trained models are evaluated their performance of raindrop removal and background information recovery of rainwater distortion of coastal wave video imagery. In order to improve the performance, we have acquired paired video dataset with and without raindrops at the real coast and conducted transfer learning to the pre-trained models with those new dataset. The performance of fine-tuned models is improved by comparing the results from pre-trained models. The performance is evaluated using the peak signal-to-noise ratio and structural similarity index and the fine-tuned Pix2Pix network by transfer learning shows the best performance to reconstruct distorted coastal wave video imagery by raindrops.

• Asian Journal of Atmospheric Environment
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• v.9 no.4
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• pp.272-279
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• 2015

The information about the dispersion and scavenging of traffic-related pollutants at the locations near busy expressways is very helpful to highway planners for developing better plans to reduce exposures to air pollution for people living as well as children attending schools and child care centers near roadways. The objective of the current study was to give information in the dispersion and scavenging of vehicle-derived pollutants at the region near a busy urban expressway by a combination of two different model calculations. The modified Gaussian dispersion model and the Lagrange type below-cloud scavenging model were applied to evaluate $NO_x$ dispersion and DEP (Diesel exhaust particles) wet removal, respectively. The highest $NO_x$ was marked 53.17 ppb within 20-30 meters from the target urban expressway during the heaviest traffic hours (08:00AM-09:00AM) and it was 2.8 times higher than that of really measured at a nearby ambient measuring station. The calculated DEP concentration in size-resolved raindrops showed a continuous decreasing with increasing raindrop size. Especially, a noticeable decrease was found between 0.2 mm and 1.0 mm raindrop diameter.