• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.163-170
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• 2013

In general, images of outdoor scenes often contain degradation due to dust, water drop, haze, fog, smoke and so on, as a result they cause the contrast reduction and color fading. Haze removal is not easier problem due to the inherent ambiguity between the haze and the underlying scene. So, we propose a novel method to solve single scene dehazing problem using the region segmentation based on graph algorithm that has used a gradient value as a cost function. We segment the scene into different regions according to depth-related information and then estimate the global atmospheric light. The medium transmission can be directly estimated by the threshold function of graph-based segmentation algorithm. After estimating the medium transmission, we can restore the haze-free scene. We evaluated the degree of the visibility restoration between the proposed method and the existing methods by calculating the gradient of the edge between the restored scene and the original scene. Results on a variety of outdoor haze scene demonstrated the powerful haze removal and enhanced image quality of the proposed method.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1303-1310
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• 2021

We propose an edge-preserving transmission estimation by comparing the patch-based dark channel and the pixel-based dark channel near the edge, in order to improve the quality of outdoor images deteriorated by conditions such as fog and smog. Moreover, we propose a refinement that applies the Guided Image Filtering (GIF), a kind of edge-preserving smoothing filtering methods, to edges using Laplacian operation for natural restoration of image objects and backgrounds, so that we can dehaze a single image and improve the visibility effectively. Experimental results carried out on various outdoor hazy images that show the proposed method has less computational complexity than the conventional methods, while reducing distortion such as halo effect, and showing excellent dehazing performance. In It can be confirmed that the proposed method can be applied to various fields including devices requiring real-time performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.67-72
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• 2013

Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.

• Journal of IKEEE
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• v.24 no.1
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• pp.243-247
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• 2020

As the autonomous driving market is rapidly growing, research on autonomous driving is being conducted. Self-driving functions should be performed regardless of the weather for the driver's safety. However, misty weather is difficult to autonomous driving because of the lack of visibility, so a defog algorithm should be used. The image obtained through the fog removal algorithm causes the image quality to deteriorate. To improve this problem, HSV color correction is used to increase the sharpness. In this paper, we propose a color correction hardware using HSV that can cope with 4K images. The hardware was designed with Verilog and verified by Modelsim. In addition, the FPGA was implemented with the goal of Xilinx's xc7z045-2ffg900.

• Tuberculosis and Respiratory Diseases
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• v.43 no.6
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• pp.882-893
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• 1996

Background : Over one-third of solitary pulmonary nodules are malignant, but most malignant SPNs are in the early stages at diagnosis and can be cured by surgical removal. Therefore, early diagnosis of malignant SPN is essential for the lifesaving of the patient. The incidence of pulmonary tuberculosis in Korea is somewhat higher than those of other countries and a large number of SPNs are found to be tuberculoma. Most primary physicians tend to regard newly detected solitary pulmonary nodule as tuberculoma with only noninvasive imaging such as CT and they prefer clinical observation if the findings suggest benignancy without further invasive procedures. Many kinds of noninvasive procedures for confirmatory diagnosis have been introduced to differentiate malignant SPNs from benign ones, but none of them has been satisfactory. FOG-PET is a unique tool for imaging and quantifying the status of glucose metabolism. On the basis that glucose metabolism is increased in the malignant transfomled cells compared with normal cells, FDG-PET is considered to be the satisfactory noninvasive procedure which can differentiate malignant SPNs from benign SPNs. So we performed FOG-PET in patients with solitary pulmonary nodule and evaluated the diagnostic accuracy in the diagnosis of malignant SPNs. Method : 34 patients with a solitary pulmonary nodule less than 6 cm of irs diameter who visited Samsung Medical Center from Semptember, 1994 to Semptember, 1995 were evaluated prospectively. Simple chest roentgenography, chest computer tomography, FOG-PET scan were performed for all patients. The results of FOG-PET were evaluated comparing with the results of final diagnosis confirmed by sputum study, PCNA, fiberoptic bronchoscopy, or thoracotomy. Results : (I) There was no significant difference in nodule size between malignant (3.1 1.5cm) and benign nodule(2.81.0cm)(p>0.05). (2) Peal SUV(standardized uptake value) of malignant nodules (6.93.7) was significantly higher than peak SUV of benign nodules(2.71.7) and time-activity curves showed continuous increase in malignant nodules. (3) Three false negative cases were found among eighteen malignant nodule by the FDG-PET imaging study and all three cases were nonmucinous bronchioloalveolar carcinoma less than 2 em diameter. (4) FOG-PET imaging resulted in 83% sensitivity, 100% specificity, 100% positive predictive value and 84% negative predictive value. Conclusion: FOG-PET imaging is a new noninvasive diagnostic method of solitary pulmonary nodule thai has a high accuracy of differential diagnosis between malignant and benign nodule. FDG-PET imaging could be used for the differential diagnosis of SPN which is not properly diagnosed with conventional methods before thoracotomy. Considering the high accuracy of FDG-PET imaging, this procedure may play an important role in making the dicision to perform thoracotomy in diffcult cases.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.119-128
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• 2014

This paper presents a new method for improving the quality of images corrupted by an external source that generates an attenuation and scattering of light like dust, water droplets and fog. Conventional defog methods typically encounter a distortion such that the restored image has low contrast and oversaturation of color in some regions because of the mis-estimated airlight and wrong media transmission. Therefore, in order to mitigate these problems, we propose a robust airlight selection method and local saturation evaluation method for estimating media transmission. The proposed method addresses the wrong media transmission and over-saturation problems caused by the mis-estimated airlight and thereby improves the restored image quality. The results of relevant experiments of the proposed method against conventional ones confirm the improved accuracy of atmospheric light estimation and the quality of restored images with regard to objective and subjective performance measures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5D
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• pp.635-644
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• 2009

Image processing technique in the outdoor environment is very sensitive, and it tends to lose a lot of accuracy when it rapidly changes by outdoor environment. Therefore, in order to calculate accurate traffic information using the traffic monitoring system, we must resolve removing shadow in transition time, Distortion by the vehicle headlights at night, noise of rain, snow, and fog, and occlusion. In the research, we developed a system to calibrate the amount of traffic, speed, and time occupancy by using image processing technique in a variety of outdoor environments change. This system were tested under outdoor environments at the Gonjiam test site, which is managed by Korea Institute of Construction Technology (www.kict.re.kr) for testing performance. We evaluated the performance of traffic information, volume counts, speed, and occupancy time, with 4 lanes (2 lanes are upstream and the rests are downstream) from the 16th to 18th December, 2008. The evaluation method performed as based on the standard data is a radar detection compared to calculated data using image processing technique. The System evaluation results showed that the amount of traffic, speed, and time occupancy in period (day, night, sunrise, sunset) are approximately 92-97% accuracy when these data compared to the standard data.