• Journal of Korea Multimedia Society
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• v.9 no.5
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• pp.542-553
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• 2006

Because Y and C signals in a composite video signal are piled one on another in the same frequency, it is impossible to separate them completely. Therefore, it is necessary to develop efficient separation technique in order to minimize degradation of video quality. In this paper, we propose wavelet-based 3D comb filter algorithm and architecture for separating Y and C signals from a composite video signal. The proposed algorithm uses wavelet transform and thresholding of compared lines for acquiring the maximum video quality. Simulation results show that the proposed algorithm has better image quality and better PSNR than previous algorithms. For real application of the proposed algorithm, we developed a hardware architecture and the architecture was implemented by using VHDL. Finally, a VLSI layout of the proposed architecture was generated by using 0.25 micrometer CMOS process.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1073-1081
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• 2009

In this paper, we proposed new method to enhance choroidal vessels by suppressing retina vessels brightness. It is well-known that CNV(choroidal neovascularization) is related with sight loss. The main feature of CNV is the occurrence of new vessels in choroid. Unfortunately, because retina vessels brightness is stronger than choroidal vessels brightness in ICGA(indocynanine green angiography) image, so that the choroidal vessels were hardly recognized. Therefore, for correct diagnosis, the choroidal vessels must be enhanced in ICGA image. The proposed enhancement method consists of 3 strategies. First, the retina vessels were detected by multi scale enhancement technique, hysteresis thresholding, KNN(Kth-nearest neighbor) classification method. And then, a retina vessel mask was generated from detection result. Next, the brightness of retina vessels was suppressed by the proposed conditional region erosion method and mask region until the mask region was vanished. Finally, the brightness of choroidal vessel was enhanced on processed image. Through an experiment, we had confirmed that the proposed method was robust and efficient.

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.227-244
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• 2015

Synthetic Aperture Radar is able to provide images of wide coverage in day, night, and all-weather conditions. Recently, as the SAR image resolution improves up to the sub-meter level, their applications are rapidly expanding accordingly. Especially there is a growing interest in the use of geographic information of high resolution SAR images and the change detection will be one of the most important technique for their applications. In this paper, an automatic threshold tracking and change detection algorithm is proposed applicable to high-resolution SAR images. To detect changes within SAR image, a reference image is generated using log-ratio operator and its amplitude distribution is estimated through K-S test. Assuming SAR image has a non-gaussian amplitude distribution, a generalized thresholding technique is applied using Kittler and Illingworth minimum-error estimation. Also, MoLC parametric estimation method is adopted to improve the algorithm performance on rough ground target. The implemented algorithm is tested and verified on the simulated SAR raw data. Then, it is applied to the spaceborne high-resolution SAR images taken by Cosmo-Skymed and KOMPSAT-5 and the performances are analyzed and compared.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.705-710
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• 2017

This paper proposes an estimation method of populations of Grapholita molestas using object segmentation and an SVM classifier in the moth images. Object segmentation and moth classification were performed on images of Grapholita molestas moth acquired on a pheromone trap equipped in an orchard. Object segmentation consisted of pre-processing, thresholding, morphological filtering, and object labeling process. The classification of Grapholita molestas in the moth images consisted of the training and classification of an SVM classifier and estimation of the moth populations. The object segmentation simplifies the moth classification process by segmenting the individual objects before passing an input image to the SVM classifier. The image blocks were extracted around the center point and principle axis of the segmented objects, and fed into the SVM classifier. In the experiments, the proposed method performed an estimation of the moth populations for 10 moth images and achieved an average estimation precision rate of 97%. Therefore, it showed an effective monitoring method of populations of Grapholita molestas in the orchard. In addition, the mean processing time of the proposed method and sliding window technique were 2.4 seconds and 5.7 seconds, respectively. Therefore, the proposed method has a 2.4 times faster processing time than the latter technique.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.111-118
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• 2004

Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data because of the reasonable performance at the low complexity. The VisuShrink which removes the noise with the universal threshold is one of the techniques. The universal threshold is proportional to the noise deviation and the number of data samples. In general, because the noise deviation is not known, one needs to estimate the deviation for determining the value of the universal threshold. But, only for the finest scale wavelet coefficients, it has been known the way of estimating the noise deviation, so the noise in coarse scales cannot be removed with the VisuShrink. We propose here a new denoising method which removes the noise in each scale except the coarsest scale by Visushrink method. The noise deviation at each band is estimated by the monotonic transform and weighted deviation, the product of estimated noise deviation by the weight, is applied to the universal threshold. By making use of the universal threshold and the Soft-Threshold technique, the noise in each band is removed. The denoising characteristics of the proposed method is compared with that of the traditional VisuShrink and SureShrink method. The result showed that the proposed method is effective in denoising on Gaussian noise and quantization noise.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.179-185
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• 2019

This paper proposes a block-type classification based image binarization for the implementation of the low-power feature extraction algorithm. The proposed method can be implemented with threshold value re-use technique approach when the image divided into $64{\times}64$ macro blocks size and calculating the threshold value for each block type only once. The algorithm is validated based on quantitative results that only a threshold value change rate of up to 9% occurs within the same image/block type. Existing algorithms should compute the threshold value for 64 blocks when the macro block is divided by $64{\times}64$ on the basis of $512{\times}512$ images, but all suggestions can be made only once for best cases where the same block type is printed, and for the remaining 63 blocks, the adaptive threshold calculation can be reduced by only performing a block type classification process. The threshold calculation operation is performed five times when all block types occur, and only the block type separation process can be performed for the remaining 59 blocks, so 93% adaptive threshold calculation operation can be reduced.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.663-670
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• 2023

In this study, we aimed to perform a comparative evaluation using quantitative factors between a region-growing based segmentation with noise reduction algorithms and a U-Net based segmentation. Initially, we applied median filter, median modified Wiener filter, and fast non-local means algorithm to computed tomography (CT) images, followed by region-growing based segmentation. Additionally, we trained a U-Net based segmentation model to perform segmentation. Subsequently, to compare and evaluate the segmentation performance of cases with noise reduction algorithms and cases with U-Net, we measured root mean square error (RMSE) and peak signal to noise ratio (PSNR), universal quality image index (UQI), and dice similarity coefficient (DSC). The results showed that using U-Net for segmentation yielded the most improved performance. The values of RMSE, PSNR, UQI, and DSC were measured as 0.063, 72.11, 0.841, and 0.982 respectively, which indicated improvements of 1.97, 1.09, 5.30, and 1.99 times compared to noisy images. In conclusion, U-Net proved to be effective in enhancing segmentation performance compared to noise reduction algorithms in CT images.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.535-544
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• 2021

Agricultural water occupies 48% of water demand, and management of agricultural reservoirs is essential for water resources management within agricultural basins. For more efficient use of agricultural water, monitoring the distribution of water resources in agricultural reservoirs and agricultural basins is required. Therefore, in this study, three threshold determination methods (i.e., fixed threshold, Otsu threshold, Kittler-Illingworth (KI) threshold) were compared to detect terrestrial water bodies using Sentinel-1 images for 3 years from 2018 to 2020. The purpose of this study was to evaluate methods for determining threshold values to more accurately estimate the reservoir area. In addition, by analyzing the relationship between the water surface and water storage at the Edong, Gosam, and Giheung reservoirs, water storage based on the SAR image was estimated and validated with observations. The thresholding method for detecting a waterbody was found to be the most accurate in the case of the KI threshold, and the water storage estimated by the KI threshold indicated a very high agreement (r = 0.9235, KGE' = 0.8691). Although the seasonal error characteristics were not observed, the problem of underestimation at high water levels may occur; the relationship between the water surface and the water storage could change rapidly. Therefore, it is necessary to understand the relationship between the water surface area and water storage through ground observation data for a more accurate estimation of water storage. If the use of SAR data through water resources satellites becomes possible in the future, based on the results of this study, it is judged that it will be beneficial for monitoring water storage and managing drought.