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Dr. Jinah Kim
Korea Institute of Ocean Science & Technology (KIOST)

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0 Climate Change Adaptation
0 Computer Science
0 Remote Sensing Applications
0 Artificial Intelligence (AI)
0 coastal disaster

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Journal article
Published: 21 October 2020 in Remote Sensing
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We propose an unsupervised network with adversarial learning, the Raindrop-aware GAN, which enhances the quality of coastal video images contaminated by raindrops. Raindrop removal from coastal videos faces two main difficulties: converting the degraded image into a clean one by visually removing the raindrops, and restoring the background coastal wave information in the raindrop regions. The components of the proposed network—a generator and a discriminator for adversarial learning—are trained on unpaired images degraded by raindrops and clean images free from raindrops. By creating raindrop masks and background-restored images, the generator restores the background information in the raindrop regions alone, preserving the input as much as possible. The proposed network was trained and tested on an open-access dataset and directly collected dataset from the coastal area. It was then evaluated by three metrics: the peak signal-to-noise ratio, structural similarity, and a naturalness-quality evaluator. The indices of metrics are 8.2% (+2.012), 0.2% (+0.002), and 1.6% (−0.196) better than the state-of-the-art method, respectively. In the visual assessment of the enhanced video image quality, our method better restored the image patterns of steep wave crests and breaking than the other methods. In both quantitative and qualitative experiments, the proposed method more effectively removed the raindrops in coastal video and recovered the damaged background wave information than state-of-the-art methods.

ACS Style

Jinah Kim; Dong Huh; Taekyung Kim; Jaeil Kim; Jeseon Yoo; Jae-Seol Shim. Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement. Remote Sensing 2020, 12, 3461 .

AMA Style

Jinah Kim, Dong Huh, Taekyung Kim, Jaeil Kim, Jeseon Yoo, Jae-Seol Shim. Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement. Remote Sensing. 2020; 12 (20):3461.

Chicago/Turabian Style

Jinah Kim; Dong Huh; Taekyung Kim; Jaeil Kim; Jeseon Yoo; Jae-Seol Shim. 2020. "Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement." Remote Sensing 12, no. 20: 3461.

Journal article
Published: 26 March 2020 in Neurocomputing
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Activation functions play important roles in determining the depth and non-linearity of deep learning models. Since the Rectified Linear Unit (ReLU) was introduced, many modifications, in which noise is intentionally injected, have been proposed to avoid overfitting. Exponential Linear Unit (ELU) and their variants, with trainable parameters, have been proposed to reduce the bias shift effects which are often observed in ReLU-type activation functions. In this paper, we propose a novel activation function, called the Elastic Exponential Linear Unit (EELU), which combines the advantages of both types of activation functions in a generalized form. EELU has an elastic slope in the positive part, and preserves the negative signal by using a small non-zero gradient. We also present a new strategy to insert neuronal noise using a Gaussian distribution in the activation function to improve generalization. We demonstrated how EELU can represent a wider variety of features with random noise than other activation functions, by visualizing the latent features of convolutional neural networks. We evaluated the effectiveness of the EELU approach through extensive experiments with image classification using the CIFAR-10/CIFAR-100, ImageNet, and Tiny ImageNet datasets. Our experimental results show that EELU achieved better generalization performance and improved classification accuracy over conventional activation functions, such as ReLU, ELU, ReLU- and ELU-like variants, Scaled ELU, and Swish. EELU produced performance improvements in image classification using a smaller number of training samples, owing to its noise injection strategy, which allows significant variation in function outputs, including deactivation.

ACS Style

Daeho Kim; Jinah Kim; Jaeil Kim. Elastic exponential linear units for convolutional neural networks. Neurocomputing 2020, 406, 253 -266.

AMA Style

Daeho Kim, Jinah Kim, Jaeil Kim. Elastic exponential linear units for convolutional neural networks. Neurocomputing. 2020; 406 ():253-266.

Chicago/Turabian Style

Daeho Kim; Jinah Kim; Jaeil Kim. 2020. "Elastic exponential linear units for convolutional neural networks." Neurocomputing 406, no. : 253-266.

Journal article
Published: 21 March 2020 in Atmosphere
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In this paper, we propose a series of procedures for coastal wave-tracking using coastal video imagery with deep neural networks. It consists of three stages: video enhancement, hydrodynamic scene separation and wave-tracking. First, a generative adversarial network, trained using paired raindrop and clean videos, is applied to remove image distortions by raindrops and to restore background information of coastal waves. Next, a hydrodynamic scene of propagated wave information is separated from surrounding environmental information in the enhanced coastal video imagery using a deep autoencoder network. Finally, propagating waves are tracked by registering consecutive images in the quality-enhanced and scene-separated coastal video imagery using a spatial transformer network. The instantaneous wave speed of each individual wave crest and breaker in the video domain is successfully estimated through learning the behavior of transformed and propagated waves in the surf zone using deep neural networks. Since it enables the acquisition of spatio-temporal information of the surf zone though the characterization of wave breakers inclusively wave run-up, we expect that the proposed framework with the deep neural networks leads to improve understanding of nearshore wave dynamics.

ACS Style

Jinah Kim; Jaeil Kim; Taekyung Kim; Dong Huh; Sofia Caires. Wave-Tracking in the Surf Zone Using Coastal Video Imagery with Deep Neural Networks. Atmosphere 2020, 11, 304 .

AMA Style

Jinah Kim, Jaeil Kim, Taekyung Kim, Dong Huh, Sofia Caires. Wave-Tracking in the Surf Zone Using Coastal Video Imagery with Deep Neural Networks. Atmosphere. 2020; 11 (3):304.

Chicago/Turabian Style

Jinah Kim; Jaeil Kim; Taekyung Kim; Dong Huh; Sofia Caires. 2020. "Wave-Tracking in the Surf Zone Using Coastal Video Imagery with Deep Neural Networks." Atmosphere 11, no. 3: 304.

Journal article
Published: 31 December 2019 in Journal of the Korea Computer Graphics Society
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사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.

ACS Style

Dong Huh; Jaeil Kim; Jinah Kim. Raindrop Removal and Background Information Recovery in Coastal Wave Video Imagery using Generative Adversarial Networks. Journal of the Korea Computer Graphics Society 2019, 25, 1 -9.

AMA Style

Dong Huh, Jaeil Kim, Jinah Kim. Raindrop Removal and Background Information Recovery in Coastal Wave Video Imagery using Generative Adversarial Networks. Journal of the Korea Computer Graphics Society. 2019; 25 (5):1-9.

Chicago/Turabian Style

Dong Huh; Jaeil Kim; Jinah Kim. 2019. "Raindrop Removal and Background Information Recovery in Coastal Wave Video Imagery using Generative Adversarial Networks." Journal of the Korea Computer Graphics Society 25, no. 5: 1-9.

Journal article
Published: 01 September 2019 in Journal of the Korea Computer Graphics Society
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ACS Style

Taekyung Kim; Jaeil Kim; Jinah Kim. Hydrodynamic scene separation from video imagery of ocean wave using autoencoder. Journal of the Korea Computer Graphics Society 2019, 25, 9 -16.

AMA Style

Taekyung Kim, Jaeil Kim, Jinah Kim. Hydrodynamic scene separation from video imagery of ocean wave using autoencoder. Journal of the Korea Computer Graphics Society. 2019; 25 (4):9-16.

Chicago/Turabian Style

Taekyung Kim; Jaeil Kim; Jinah Kim. 2019. "Hydrodynamic scene separation from video imagery of ocean wave using autoencoder." Journal of the Korea Computer Graphics Society 25, no. 4: 9-16.