• Title/Summary/Keyword: cGAN

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Segmenting Layers of Retinal OCT Images using cGAN (cGAN을 이용한 OCT 이미지의 층 분할)

  • Kwon, Oh-Heum;Kwon, Ki-Ryong;Song, Ha-Joo
    • Journal of Korea Multimedia Society
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    • v.23 no.12
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    • pp.1476-1485
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    • 2020
  • Segmenting OCT retinal images into layers is important to diagnose and understand the progression of retinal diseases or identify potential symptoms. The task of manually identifying these layers is a difficult task that requires a lot of time and effort even for medical professionals, and therefore, various studies are being conducted to automate this using deep learning technologies. In this paper, we use cGAN-based neural network to automatically segmenting OCT retinal images into seven terrain-type regions defined by six layer boundaries. The network is composed of a Segnet-based generator model and a discriminator model. We also proposed a dynamic programming algorithm for refining the outputs of the network. We performed experiments using public OCT image data set and compared its performance with the Segnet-only version of the network. The experimental results show that the cGAN-based network outperforms Segnet-only version.

Development of hybrid precipitation nowcasting model by using conditional GAN-based model and WRF (GAN 및 물리과정 기반 모델 결합을 통한 Hybrid 강우예측모델 개발)

  • Suyeon Choi;Yeonjoo Kim
    • Proceedings of the Korea Water Resources Association Conference
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    • 2023.05a
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    • pp.100-100
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    • 2023
  • 단기 강우 예측에는 주로 물리과정 기반 수치예보모델(NWPs, Numerical Prediction Models) 과 레이더 기반 확률론적 방법이 사용되어 왔으며, 최근에는 머신러닝을 이용한 레이더 기반 강우예측 모델이 단기 강우 예측에 뛰어난 성능을 보이는 것을 확인하여 관련 연구가 활발히 진행되고 있다. 하지만 머신러닝 기반 모델은 예측 선행시간 증가 시 성능이 크게 저하되며, 또한 대기의 물리적 과정을 고려하지 않는 Black-box 모델이라는 한계점이 존재한다. 본 연구에서는 이러한 한계를 극복하기 위해 머신러닝 기반 blending 기법을 통해 물리과정 기반 수치예보모델인 Weather Research and Forecasting (WRF)와 최신 머신러닝 기법 (cGAN, conditional Generative Adversarial Network) 기반 모델을 결합한 Hybrid 강우예측모델을 개발하고자 하였다. cGAN 기반 모델 개발을 위해 1시간 단위 1km 공간해상도의 레이더 반사도, WRF 모델로부터 산출된 기상 자료(온도, 풍속 등), 유역관련 정보(DEM, 토지피복 등)를 입력 자료로 사용하여 모델을 학습하였으며, 모델을 통해 물리 정보 및 머신러닝 기반 강우 예측을 생성하였다. 이렇게 생성된cGAN 기반 모델 결과와 WRF 예측 결과를 결합하는 머신러닝 기반 blending 기법을 통해Hybrid 강우예측 결과를 최종적으로 도출하였다. 본 연구에서는 Hybrid 강우예측 모델의 성능을 평가하기 위해 수도권 및 안동댐 유역에서 발생한 호우 사례를 기반으로 최대 선행시간 6시간까지 모델 예측 결과를 분석하였다. 이를 통해 물리과정 기반 모델과 머신러닝 기반 모델을 결합하는 Hybrid 기법을 적용하여 높은 정확도와 신뢰도를 가지는 고해상도 강수 예측 자료를 생성할 수 있음을 확인하였다.

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Resolution Conversion of SAR Target Images Using Conditional GAN (Conditional GAN을 이용한 SAR 표적영상의 해상도 변환)

  • Park, Ji-Hoon;Seo, Seung-Mo;Choi, Yeo-Reum;Yoo, Ji Hee
    • Journal of the Korea Institute of Military Science and Technology
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    • v.24 no.1
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    • pp.12-21
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    • 2021
  • For successful automatic target recognition(ATR) with synthetic aperture radar(SAR) imagery, SAR target images of the database should have the identical or highly similar resolution with those collected from SAR sensors. However, it is time-consuming or infeasible to construct the multiple databases with different resolutions depending on the operating SAR system. In this paper, an approach for resolution conversion of SAR target images is proposed based on conditional generative adversarial network(cGAN). First, a number of pairs consisting of SAR target images with two different resolutions are obtained via SAR simulation and then used to train the cGAN model. Finally, the model generates the SAR target image whose resolution is converted from the original one. The similarity analysis is performed to validate reliability of the generated images. The cGAN model is further applied to measured MSTAR SAR target images in order to estimate its potential for real application.

GAN using Frequency Domain (주파수 영역을 활용한 GAN)

  • Chae-Eun Lee;Sung Hoon Jung
    • Proceedings of the Korea Information Processing Society Conference
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    • 2023.05a
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    • pp.567-569
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    • 2023
  • GAN은 이미지 생성모델로서 이미지 공간에서 좋은 결과를 보여왔다. 우리는 이러한 GAN의 능력을 더욱 향상하기 위하여 본 연구에서 주파수 영역에서 이미지를 학습하고 생성하는 새로운 방법을 제안한다. 이를 위하여 먼저 학습데이터를 2D FFT로 주파수 영역으로 변환한 후 변환된 학습데이터를 GAN이 학습하게 한다. 학습 후에 GAN은 새로운 이미지를 생성하며 생성된 이미지를 2D IFFT하여 이미지 공간으로 변환한다. 이렇게 주파수 영역에서 이미지를 생성하는 방법은 이미지 공간에서 생성하는 방법보다 다양한 장점이 있다. 생성된 이미지의 품질을 평가하기 위하여 4개 데이터 셋에 4개의 평가지표를 사용하여 평가한 결과 주파수 영역에서 생성한 이미지가 IS, P&R, D&C 측면에서 더 좋은 것으로 평가되었다.

Frontal Face Generation Algorithm from Multi-view Images Based on Generative Adversarial Network

  • Heo, Young- Jin;Kim, Byung-Gyu;Roy, Partha Pratim
    • Journal of Multimedia Information System
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    • v.8 no.2
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    • pp.85-92
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    • 2021
  • In a face, there is much information of person's identity. Because of this property, various tasks such as expression recognition, identity recognition and deepfake have been actively conducted. Most of them use the exact frontal view of the given face. However, various directions of the face can be observed rather than the exact frontal image in real situation. The profile (side view) lacks information when comparing with the frontal view image. Therefore, if we can generate the frontal face from other directions, we can obtain more information on the given face. In this paper, we propose a combined style model based the conditional generative adversarial network (cGAN) for generating the frontal face from multi-view images that consist of characteristics that not only includes the style around the face (hair and beard) but also detailed areas (eye, nose, and mouth).

Comparison of Seismic Data Interpolation Performance using U-Net and cWGAN (U-Net과 cWGAN을 이용한 탄성파 탐사 자료 보간 성능 평가)

  • Yu, Jiyun;Yoon, Daeung
    • Geophysics and Geophysical Exploration
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    • v.25 no.3
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    • pp.140-161
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    • 2022
  • Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.

Comparison of CNN and GAN-based Deep Learning Models for Ground Roll Suppression (그라운드-롤 제거를 위한 CNN과 GAN 기반 딥러닝 모델 비교 분석)

  • Sangin Cho;Sukjoon Pyun
    • Geophysics and Geophysical Exploration
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    • v.26 no.2
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    • pp.37-51
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    • 2023
  • The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.

Combining Conditional Generative Adversarial Network and Regression-based Calibration for Cloud Removal of Optical Imagery (광학 영상의 구름 제거를 위한 조건부 생성적 적대 신경망과 회귀 기반 보정의 결합)

  • Kwak, Geun-Ho;Park, Soyeon;Park, No-Wook
    • Korean Journal of Remote Sensing
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    • v.38 no.6_1
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    • pp.1357-1369
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    • 2022
  • Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.

Nitric Oxide, TNF-${\alpha}$ and TGF-${\beta}$ Formation of Rat Kupffer Cell Activated by the ${\beta}$-Glucan from Ganoderma lucidum (영지의 ${\beta}$-glucan성 다당류에 의해 활성화된 흰쥐 간내 Kupffer 세포의 NO, TNF-${\alpha}$ 및 TGF-${\beta}$ 형성)

  • Han, Man-Deuk;Lee, June-Woo;Jeong, Hoon;Kim, Yong-Seok;Ra, Su-Jung;Yoon, Kyung-Ha
    • Microbiology and Biotechnology Letters
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    • v.27 no.1
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    • pp.28-34
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    • 1999
  • Ganoderan (GAN), an immunomodulating ${\beta}$-glucan from mushroom Ganoderma lucidum, was evaluated for its ability to induce formation of nitric oxide (NO), tumor necrosis factor-${\alpha}$(TNF-${\alpha}$) and transforming growth factor (TGF-${\beta}$) from rat Kupffer cell in vitro. Hepatic macrophages activated by GAN significantly elevated concentration of NO and TNF-${\alpha}$ in cultured medium, but not significantly elevated that of TGF-${\beta}$. GAN-activated Kupffer cells secrete 14.9${\mu}$M (p<0.01) of NO and 2619.5${\rho}$g/ml (p<0.01) of TNF-${\alpha}$after 36hr of incubation at 37$^{\circ}C$. The results revealed that GAN enhanced 4-fold production of NO and 19 fold formation of TNF-${\alpha}$ compared to the control. The proliferation of GAN-activated Kupffer cells was inhibited as compared with its negative control. Comparing the activity among glucans derived from microorganisms, highly branched zymosan, glucomannan from Saccharomyces cerevisiae, significantly increased TNF-${\alpha}$ and NO production. These results indicate that the ${\beta}$-glucan from G. lucidum activates rat Kupffer cell and secretes NO and TNF-${\alpha}$. It also suggest that rat Kupffer cell posses certain receptor for ${\beta}$-anomeric glucan.

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Denoise of Astronomical Images with Deep Learning

  • Park, Youngjun;Choi, Yun-Young;Moon, Yong-Jae;Park, Eunsu;Lim, Beomdu;Kim, Taeyoung
    • The Bulletin of The Korean Astronomical Society
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    • v.44 no.1
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    • pp.54.2-54.2
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    • 2019
  • Removing noise which occurs inevitably when taking image data has been a big concern. There is a way to raise signal-to-noise ratio and it is regarded as the only way, image stacking. Image stacking is averaging or just adding all pixel values of multiple pictures taken of a specific area. Its performance and reliability are unquestioned, but its weaknesses are also evident. Object with fast proper motion can be vanished, and most of all, it takes too long time. So if we can handle single shot image well and achieve similar performance, we can overcome those weaknesses. Recent developments in deep learning have enabled things that were not possible with former algorithm-based programming. One of the things is generating data with more information from data with less information. As a part of that, we reproduced stacked image from single shot image using a kind of deep learning, conditional generative adversarial network (cGAN). r-band camcol2 south data were used from SDSS Stripe 82 data. From all fields, image data which is stacked with only 22 individual images and, as a pair of stacked image, single pass data which were included in all stacked image were used. All used fields are cut in $128{\times}128$ pixel size, so total number of image is 17930. 14234 pairs of all images were used for training cGAN and 3696 pairs were used for verify the result. As a result, RMS error of pixel values between generated data from the best condition and target data were $7.67{\times}10^{-4}$ compared to original input data, $1.24{\times}10^{-3}$. We also applied to a few test galaxy images and generated images were similar to stacked images qualitatively compared to other de-noising methods. In addition, with photometry, The number count of stacked-cGAN matched sources is larger than that of single pass-stacked one, especially for fainter objects. Also, magnitude completeness became better in fainter objects. With this work, it is possible to observe reliably 1 magnitude fainter object.

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