• 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.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.806-817
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• 2015

The contrast limited adaptive histogram equalization(CLAHE) is an advanced method for the histogram equalization which is a common contrast enhancement technique. The CLAHE divides the image into sections, and applies the contrast limited histogram equalization for each section. X-ray images can be classified into three areas: skin, bone, and air area. In clinical application, the interest area is limited to the skin or bone area depending on the diagnosis region. The CLAHE could deteriorate X-ray image quality because the CLAHE enhances the area which doesn't need to be enhanced. In this paper, we propose a new method which automatically determines the clip limit of CLAHE's parameter to improve X-ray image quality using fuzzy logic. We introduce fuzzy logic which is possible to determine clip limit proportional to the interest of users. Experimental results show that the proposed method improve images according to the user's preference by focusing on the subject.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1231-1236
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• 2006

The contrast control of images with narrow dynamic range is a simple method among enhancement methods for low intensity of image. Histogram equalization is the most common method for this purpose, which stretches the dynamic range of intensity Conventional methods would fail to enhance images with extremely dark and bright regions, because of not considering the shape of histogram. In this paper, we propose a novel adaptive histogram equalization based on histogram matching with multiple Gaussian transformation function. As a result, output images with a couple of peaks of histogram could be improved and the details such as edges in dark regions could be appeared better than conventional method subjectively.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.83-86
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• 2019

Histogram equalization is extensively used for image contrast enhancement in various applications due to its effectiveness and its modest functions. In image research, image enhancement is one of the most significant and arduous technique. The image enhancement aim is to improve the visual appearance of an image. Different kinds of images such as satellite images, medical images, aerial images are affected from noise and poor contrast. So it is important to remove the noise and improve the contrast of the image. Therefore, for this purpose, we apply a median filter on MR image as the median filter remove the noise and preserve the edges effectively. After applying median filter on MR image we have used intensity transformation function on the filtered image to increase the contrast of the image. Than applied the histogram equalization (HE) technique on the filtered image. The simple histogram equalization technique over enhances the brightness of the image due to which the important information can be lost. Therefore, adaptive histogram equalization (AHE) and contrast limited histogram equalization (CLAHE) techniques are used to enhance the image without losing any information.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.361-362
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• 2007

We propose a contrast-controlled feature detection approach for steel radiograph image. X-ray images are low contrast, dark and high noise image. So, It is not simple to detect defects directly in automated radiography inspection system. Contrast enhancement, histogram equalization and median filter are the most frequently used techniques to enhance the X-ray images. In this paper, the adaptive control method based on contrast limited histogram equalization is compared with several histogram techniques. Through comparative analysis, CLAHE(contrast controlled adaptive histogram equalization) can enhance detection of defects better.

• Journal of radiological science and technology
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• v.44 no.5
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• pp.473-480
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• 2021

This study was purpose to method that applies for improving the image quality in CT and X-ray scan, especially in the lung region. Also, we researched the parameters of the image before and after applying for Histogram Equalization (HE) such as mean, median values in the histogram. These techniques are mainly used for all type of medical images such as for Chest X-ray, Low-Dose Computed Tomography (CT). These are also used to intensify tiny anatomies like vessels, lung nodules, airways and pulmonary fissures. The proposed techniques consist of two main steps using the MATLAB software (R2021a). First, the technique should apply for the process of normalization for improving the basic image more correctly. In the next, the technique actively rearranges the intensity of the image contrast. Second, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method was used for enhancing small details, textures and local contrast of the image. As a result, this paper shows the modern and improved techniques of HE and some advantages of the technique on the traditional HE. Therefore, this paper concludes that various techniques related to the HE can be helpful for many processes, especially image pre-processing for Machine Learning (ML), Deep Learning (DL).

• Progress in Medical Physics
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• v.14 no.3
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• pp.196-202
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• 2003

In 3-dimensional conformal radiation therapy (3D-CRT) and intensity-modulated radiation therapy (IMRT), many studies on reducing setup error have been conducted in order to focus the irradiation on the tumors while sparing normal tissues as much as possible. As one of these efforts, we developed an image enhancement and registration tool for simulators and portal images that analyze setup errors in a quantitative manner. For setup verification, we used simulator (films and EC-L films (Kodak, USA) as portal images. In addition, digital-captured images during simulation, and digitally-reconstructed radiographs (DRR) can be used as reference images in the software, which is coded using IDL5.4 (Research Systems Inc., USA). To improve the poor contrast of portal images, histogram-equalization, and adaptive histogram equalization, CLAHE (contrast limited adaptive histogram equalization) was implemented in the software. For image registration between simulator and portal images, contours drawn on the simulator image were transferred into the portal image, and then aligned onto the same anatomical structures on the portal image. In conclusion, applying CLAHE considerably improved the contrast of portal images and also enabled the analysis of setup errors in a quantitative manner.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.65-73
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• 2011

In this paper, we present a novel digital multifocusing approach using adaptive region-based histogram equalization for the multiple color-filter aperture (MCA) system with insufficient amount of incoming light. From the image acquired by the MCA system, we can estimate the depth information of objects at different distances by measuring the amount of misalignment among the RGB color planes. The estimated depth information is used to obtain multifocused images together with the process of the region-of-interests (ROIs) classification, registration, and fusion. However, the MCA system results in the low-exposure problem because of the limited size of the apertures. For overcoming this problem, we propose adaptive region-based histogram equalization. Based on the experimental results, the proposed algorithm is proved to be able to obtain in-focused images under the low light level environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1378-1387
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• 2013

Histogram equalization, which stretches the dynamic range of intensity, is the most common method for enhancing the contrast of image. Contrast limited adaptive histogram equalization(CLAHE), proposed by K. Zuierveld, has two key parameters: block size and clip limit. These parameters mainly control image quality, but have been heuristically determined by user. In this paper, we propose a novel method of determining two parameters of CLAHE using entropy of image. The key idea is based on the characteristics of entropy curves: clip limit vs entropy and block size vs entropy. Clip limit and block size are determined at the point with maximum curvature on entropy curve. Experimental results show that the proposed method improves images with very low contrast.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.345-348
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• 1997

Endoscope image is the shape that a doctor sees inside of patient through endoscope. The characteristics of these images are much effected by the light source of endoscope, specially areas in short distance from a light have much light source and look clear, but areas in long distance from a light look dark relatively because of little light quantity. So we developed a new level adaptive image enhancer for the dark area in a endoscope image. The algorithm we made consists of three parts ; 1) Classification of histogram in segmented area 2) Smoothing and Adaptive Histogram Equalization 3) Adaptive Histogram Modification.