• Journal of the Korean Society of Radiology
• /
• v.16 no.2
• /
• pp.169-175
• /
• 2022

In this study, envisioned a laboratory equipped with virus blocking equipment for chest X-ray examinations of respiratory or droplet-transmitted virus-infected patients, and the material with the least deterioration in X-ray output and image quality among the proven blocking materials that block viruses in the design process. and experimented to find the thickness. As a result, when 1 cm of acrylic was applied, the X-ray output was reduced by only about 3.27 % compared to the absence of the barrier material, the SNR was 40.7 and CNR was 30.9, which was the best. The SSIM index result was analyzed as 0.891, which was analyzed to be implemented as the most similar image compared to the original image. The barrier material applied in the research method was objective in that it used a product approved by the Ministry of Food and Drug Safety. the results of this study are expected to provide useful information when installing X-ray examination facilities for the diagnosis and treatment of respiratory-related virus-infected patients in the future.

• Geophysics and Geophysical Exploration
• /
• v.26 no.2
• /
• pp.37-51
• /
• 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.

• Journal of Broadcast Engineering
• /
• v.27 no.1
• /
• pp.3-12
• /
• 2022

Image deblurring aims to remove image blur, which can be generated while shooting the pictures by the movement of objects, camera shake, blurring of focus, and so forth. With the rise in popularity of smartphones, it is common to carry portable digital cameras daily, so image deblurring techniques have become more significant recently. Originally, image deblurring techniques have been studied using traditional optimization techniques. Then with the recent attention on deep learning, deblurring methods based on convolutional neural networks have been actively proposed. However, most of them have been developed while focusing on better performance. Therefore, it is not easy to use in real situations due to the speed of their algorithms. To tackle this problem, we propose a novel deep learning-based deblurring algorithm that can be operated in real-time on HD resolution. In addition, we improved the training and inference process and could increase the performance of our model without any significant effect on the speed and the speed without any significant effect on the performance. As a result, our algorithm achieves real-time performance by processing 33.74 frames per second at 1280×720 resolution. Furthermore, it shows excellent performance compared to its speed with a PSNR of 29.78 and SSIM of 0.9287 with the GoPro dataset.

• Journal of the Korean Society of Radiology
• /
• v.17 no.5
• /
• pp.693-699
• /
• 2023

Ultrasound is widely used in the medical field for non-destructive and non-invasive disease diagnosis. In order to improve the disease diagnosis accuracy of diagnostic medical images, improving spatial resolution is a very important factor. In this study, we aim to model the super resolution convolutional neural network (SRCNN) algorithm in ultrasound images and analyze its applicability in the medical diagnostic field. The study was conducted as an experimental study using Field II simulation and open source clinical liver hemangioma ultrasound imaging. The proposed SRCNN algorithm was modeled so that end-to-end learning can be applied from low resolution (LR) to high resolution. As a result of the simulation, we confirmed that the full width at half maximum in the phantom image using a Field II program was improved by 41.01% compared to LR when SRCNN was used. In addition, the peak to signal to noise ratio (PSNR) and structural similarity index (SSIM) evaluation results showed that SRCNN had the excellent value in both simulated and real liver hemangioma ultrasound images. In conclusion, the applicability of SRCNN to ultrasound images has been proven, and we expected that proposed algorithm can be used in various diagnostic medical fields.

• Journal of Korea Water Resources Association
• /
• v.56 no.10
• /
• pp.641-653
• /
• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.2
• /
• pp.109-118
• /
• 2024

With the increasing number of social media users worldwide, cases of social media being abused to perpetrate various crimes are increasing. Specifically, drug distribution through social media is emerging as a serious social problem. Using social media channels, the curiosity of teenagers regarding drugs is stimulated through clever marketing. Further, social media easily facilitates drug purchases due to the high accessibility of drug sellers and consumers. Among various social media platforms, we focused on Instagram, which is the most used social media platform by young adults aged 19 to 24 years in South Korea. We collected four types of information, including profile photos, introductions, posts in the form of images, and posts in the form of texts on Instagram; then, we analyzed the similarity among each type of collected information. The profile photos and posts in the form of image were analyzed for similarity based on the SSIM(Structural Simplicity Index Measure), while introductions and posts in the form of text were analyzed for similarity using Jaccard and Cosine similarity techniques. Through the similarity analysis, the similarity among various accounts for each collected information type was measured, and accounts with similarity above the significance level were determined as the same drug sales account. By performing logistic regression analysis on the aforementioned information types, we confirmed that except posts in image form, profile photos, introductions, and posts in the text form were valid information for tracking the same drug sales account.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.5
• /
• pp.21-29
• /
• 2024

Reducing the radiation dose during CT scanning can lower the risk of radiation exposure, but not only does the image resolution significantly deteriorate, but the effectiveness of diagnosis is reduced due to the generation of noise. Therefore, noise removal from CT images is a very important and essential processing process in the image restoration. Until now, there are limitations in removing only the noise by separating the noise and the original signal in the image area. In this paper, we aim to effectively remove noise from CT images using the wavelet transform-based GAN model, that is, the WT-GAN model in the frequency domain. The GAN model used here generates images with noise removed through a U-Net structured generator and a PatchGAN structured discriminator. To evaluate the performance of the WT-GAN model proposed in this paper, experiments were conducted on CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. As a result of the performance experiment, the WT-GAN model is better than the traditional filter, that is, the BM3D filter, as well as the existing deep learning models, such as DnCNN, CDAE model, and U-Net GAN model, in qualitative and quantitative measures, that is, PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure) showed excellent results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.3
• /
• pp.1011-1025
• /
• 2014

A demosaicing method for a Bayer-like W-RGB color filter array (CFA) is proposed. When reproducing images from a W-RGB CFA, conventional color separation methods for W-RGB CFA are likely to cause blurring near the edges due to rough averaging using a color ratio of neighboring pixels. Moreover, these methods cannot be applied to real-life digital cameras with W-RGB CFA because the methods were proposed under an ideal situation, W=R+G+B, not a real-life situation, $W{\neq}R+G+B$. To improve edge performance, we propose a method of constant color difference assumption with inversed weight, which uses information from all edge directions for interpolating all missing color channels. The proposed method calculates the correlation between W, R, G, and B to enable its application to real-life digital cameras with W-RGB CFA. Simulations were performed to evaluate the proposed method using images captured from a real-life digital camera with W-RGB CFA. Simulation results shows that we can demosaic by using the proposed algorithm compared with the conventional one in about +34.79% SNR, +11.43% PSNR, +1.54% SSIM and 14.02% S-CIELAB error. Thus, the proposed method demosaics better than the conventional methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.4
• /
• pp.319-326
• /
• 2017

SAR (Synthetic aperture radar) images are less affected by the weather compared to optical images and can be obtained at any time of the day. Therefore, SAR images are being actively utilized for military applications and natural disasters. However, because SAR data are in grayscale, it is difficult to perform visual analysis and to decipher details. In this study, we propose a color mapping method using RF (random forest) regression for enhancing the visual decipherability of SAR images. COSMO-SkyMed 2 and WorldView-3 images were obtained for the same area and RF regression was used to establish color configurations for performing color mapping. The results were compared with image fusion, a traditional color mapping method. The UIQI (universal image quality index), the SSIM (structural similarity) index, and CC (correlation coefficients) were used to evaluate the image quality. The color-mapped image based on the RF regression had a significantly higher quality than the images derived from the other methods. From the experimental result, the use of color mapping based on the RF regression for SAR images was confirmed.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.2
• /
• pp.519-528
• /
• 2016

In order to achieve fast computational speed with good visual quality of output video, we propose a frequency domain based new fractal video compression scheme. Normalized cross correlation is used to find the structural self similar domain block for the input range block. To increase the searching speed, cross correlation is implemented in the frequency domain using FFT with one computational operation for all the domain blocks instead of individual block wise calculations. The encoding time is further minimized by applying rotation and reflection DFT properties to the IFFT of zero padded range blocks. The energy of overlap small size domain blocks is pre-computed for the entire reference frame and retaining the energies of the overlapped search window portion of previous adjacent block. Quadtree decompositions are obtained by using domain block motion compensated prediction error as a threshold to control the further partitions of the block. It provides a better level of adaption to the scene contents than fixed block size approach. The result shows that, on average, the proposed method can raise the encoding speed by 48.8 % and 90 % higher than NHEXS and CPM/NCIM algorithms respectively. The compression ratio and PSNR of the proposed method is increased by 15.41 and 0.89 dB higher than that of NHEXS on average. For low bit rate videos, the proposed algorithm achieve the high compression ratio above 120 with more than 31 dB PSNR.