• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.702-712
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• 2017

In this paper, we propose an unsharp masking algorithm using Laplacian as a weight map for the signal structure and a gamma transformation algorithm using image mean intensity as a weight map for mean intensity. The conventional weight map based on the patch has a disadvantage in that the brightness in the image is shifted to one side in the signal structure and the mean intensity region. So the detailed information is lost. In this paper, we improved the detail using unsharp masking of patch unit and proposed linearly combined the gamma transformed values using the average brightness values of the global and local images. Through the proposed algorithm, the detail information such as edges are preserved and the subjective image quality is improved by adjusting the brightness of the light. Experiment results show that the proposed algorithm show better performance than conventional algorithm.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.1
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• pp.14-21
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• 2008

In this paper, we propose an adaptive contrast enhancement method using dynamic range segmentation. Histogram Equalization (HE) method is widely used for contrast enhancement. However, histogram equalization method is not suitable for commercial display because it may cause undesirable artifacts due to the significant change in brightness. The proposed algorithm segments the dynamic range of the histogram and redistributes the pixel intensities by the segment area ratio. The proposed method may cause over compressed effect when intensity distribution of an original image is concentrated in specific narrow region. In order to overcome this problem, we introduce an adaptive scale factor. The experimental results show that the proposed algorithm suppresses the significant change in brightness and provides wide histogram distribution compared with histogram equalization.

• Science of Emotion and Sensibility
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• v.20 no.1
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• pp.57-66
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• 2017

Emotional image quality optimization methodologies are investigated using technological image quality controls based on the eye tests of various image samples. The images are evaluated based on various contrast, lightness and saturation image quality metric tone curves. The order of importance to image quality enhancements is contrast, saturation and brightness. The slopes of emotional image qualities with respect to technical image quality metric changes are found to be composed of mathematical function modelling with nearly zero, intermediate and maximum slope regions in general, which can reflect well known log and saturated as well as conventional reverse U shape natures. Image quality improvements are analyzed not only with just single but also with multiple image quality metrics. To ease the unified image quality metric analysis and control, a new function is presented to utilize both the newly found and conventional emotional image quality behaviors. It is found that the overall image quality enhancement can be realized only in a few limited cases of multiple image quality metric controls. It is also found that the kinds of image quality enhancement methodologies are not strongly dependent on image contents (genre).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3188-3207
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• 2017

In this paper, we propose an image enhancement algorithm to improve license plate extraction rate in various environments (Day Street, Night Street, Underground parking lot, etc.). The proposed algorithm is composed of image enhancement algorithm and license plate extraction algorithm. The image enhancement method can improve an image quality of the degraded image, which utilizes a histogram information and overall gray level distribution of an image. The proposed algorithm employs an interpolated probability distribution value (PDV) in order to control a sudden change in image brightness. Probability distribution value can be calculated using cumulative distribution function (CDF) and probability density function (PDF) of the captured image, whose values are achieved by brightness distribution of the captured image. Also, by adjusting the image enhancement factor of each part region based on image pixel information, it provides a function that can adjust the gradation of the image in more details. This processed gray image is converted into a binary image, which fuses narrow breaks and long thin gulfs, eliminates small holes, and fills gaps in the contour by using morphology operations. Then license plate region is detected based on aspect ratio and license plate size of the bound box drawn on connected license plate areas. The images have been captured by using a video camera or a personal image recorder installed in front of the cars. The captured images have included several license plates on multilane roads. Simulation has been executed using OpenCV and MATLAB. The results show that the extraction success rate is more improved than the conventional algorithms.

• Journal of radiological science and technology
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• v.39 no.2
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• pp.157-161
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• 2016

The purpose of this study is that in measures the acoustic propagate characteristics and temperature sensitivity of ultrasound tissue mimicking phantom(TM phantom). TM phantom manufacture according to the International Electronical Committee(IEC) guidelines for acoustic propagate characteristics of soft tissue. TM phantom was observed to have the image brightness and the image depth penetration decreases changes convergence which was the subject of ultrasound image characteristics in accordance with an external temperature that the change is reduced in temperature below $22^{\circ}C$. This study provide a basis t o create another TM Phantom and TM Phantom has been determined that it is appropriate for use in more than $22^{\circ}C$.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.462-470
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• 2008

This study applies fuzzy functions to improve image quality under the assumption that uncertainty of image information due to low contrast is based on vagueness and ambiguity of the brightness pixel values. To solve the problem of low contrast images whose brightness distribution is inclined, we use the k-means algorithm as a parameter of the fuzzy function, through which automatic critical points can be found to differentiate objects from background and contrast between bright and dark points can be improved. The fuzzy function is presented at the three main stages presented to improve image quality: fuzzification, contrast enhancement and defuzzification. To measure improved image quality, we present the fuzzy index and entropy index and in comparison with those of histogram equalization technique, it shows outstanding performance.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.108-112
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• 2023

Precision manufacturing technology is rapidly developing due to the extreme miniaturization of semiconductor processes to comply with Moore's Law. Accurate and precise alignment, which is one of the key elements of the semiconductor pre-process and post-process, is very important in the semiconductor process. The center detection of wafer align marks plays a key role in improving yield by reducing defects and research on accurate detection methods for this is necessary. Methods for accurate alignment using traditional image sensors can cause problems due to changes in image brightness and noise. To solve this problem, engineers must go directly into the line and perform maintenance work. This paper emphasizes that the development of AI technology can provide innovative solutions in the semiconductor process as high-resolution image and image processing technology also develops. This study proposes a new wafer center detection method through variable thresholding. And this study introduces a method for detecting the center that is less sensitive to the brightness of LEDs by utilizing a high-performance object detection model such as YOLOv8 without relying on existing algorithms. Through this, we aim to enable precise wafer focus detection using artificial intelligence.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.28 no.2
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• pp.415-433
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• 1998

Digora system is an intraoral indirect digital radiography system utilizing storage phosphor image plate. It has wide dynamic range which allows it to decrease the patient s exposure time and may increase diagnostic ability through image processing (such as edge enhancement, grey scale conversion, brightness change, and contrast enhancement). And also, it can transmit and storage image information. The purpose of this study was to evaluate the diagnostic ability of artificial proximal caries between Conventional radiograph and Digora images(unenhanced image, brightness & contrast controlled image, and edge enhanced image). ROC(Receiver Operating Characteristic) analysis, paired t-tests, and F-tests were done for the statistical evaluation of detectability. The following results were acquired: 1. In Grade I lesions, the mean ROC areas of Conventional radiograph, Digora unenhanced image, Digora controlled image, and Digora edge enhanced image were 0.953, 0.933, 0.965, 0.978 (p>0.05). 2. In Grade II lesions, the mean ROC areas of Conventional radiograph, Digora unenhanced image, Digora controlled image, and Digora edge enhanced image were 0.969, 0.964, 0.988, 0.994. Among theses areas, there was just statistical significance between Diagnostic abilities of Digora edge enhanced image and Conventional radiograph (p<0.05). 3. In the Interobserver variability, the ROC curve areas of Digora edge enhanced image was lowermost in these areas, regardless of the Carious lesion depths. In conclusion, intraoral indirect digital system, Digora system, has the potential possibility as an alternative of Conventional radiograph in the diagnosis of proximal caries.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.96-110
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• 2017

Most of vehicle detection studies using the existing general lens or wide-angle lens have a blind spot in the rear detection situation, the image is vulnerable to noise and a variety of external environments. In this paper, we propose a method that is detection in harsh external environment with noise, blind spots, etc. First, using a fish-eye lens will help minimize blind spots compared to the wide-angle lens. When angle of the lens is growing because nonlinear radial distortion also increase, calibration was used after initializing and optimizing the distortion constant in order to ensure accuracy. In addition, the original image was analyzed along with calibration to remove fog and calibrate brightness and thereby enable detection even when visibility is obstructed due to light and dark adaptations from foggy situations or sudden changes in illumination. Fog removal generally takes a considerably significant amount of time to calculate. Thus in order to reduce the calculation time, remove the fog used the major fog removal algorithm Dark Channel Prior. While Gamma Correction was used to calibrate brightness, a brightness and contrast evaluation was conducted on the image in order to determine the Gamma Value needed for correction. The evaluation used only a part instead of the entirety of the image in order to reduce the time allotted to calculation. When the brightness and contrast values were calculated, those values were used to decided Gamma value and to correct the entire image. The brightness correction and fog removal were processed in parallel, and the images were registered as a single image to minimize the calculation time needed for all the processes. Then the feature extraction method HOG was used to detect the vehicle in the corrected image. As a result, it took 0.064 seconds per frame to detect the vehicle using image correction as proposed herein, which showed a 7.5% improvement in detection rate compared to the existing vehicle detection method.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1391-1394
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• 1997

This paper proposes a new image processing algorithm to recognize korean documents. It take out the region of text area form input image, then it makes esgmentation of lines, words and characters in the text. A precision segmentation is very important to recognize the input document. The input image has 8-bit gray scaled resolution. Not only the histogram but also brightness dispersion graph are used for segmentation. The result shows a higher accuracy of document recognition.