• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.155-164
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• 2014

Local histogram equalization is one of the most popular ways of enhancing the local brightness features of an input image. However, local histogram equalization reveals some problems. First, undesired artifacts are produced by over-enhancing the local features. Second, the enhancement of local features does not always result in global contrast enhancement. To cope with these problems, we propose an illumination driven local histogram equalization method. First, to estimate the illumination information, the proposed method combines the input image and the blurred image produced through the process of the down-sampling and the up-sampling. Next, the proposed method adaptively adjusts the mapping function estimated by the local histogram equalization using the information of the illumination. As a result, the proposed illumination information driven local histogram equalization method simultaneously enhances the global and the local contrast levels while preventing any local artifacts. Experimental results show that the proposed algorithm outperforms the conventional methods on objective and subjective criteria.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.193-198
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• 2010

In this paper, an efficient contrast enhancement algorithm using histogram stretching is proposed. Histogram equalization (HE) and histogram stretching (HS) are effective techniques for contrast enhancement. However, HE and HS result often in excessive contrast enhancement. Proposed technique not only produces better results than those of conventional contrast enhancement techniques, but is also adaptively adjusted to image contents.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.531-534
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• 2008

Image Hash specifies as a descriptor that can be used to measure similarity in images. Among all image Hash methods, histogram based image Hash has robustness to common noise-like operation and various geometric except histogram _equalization. In this_paper an improved histogram based Image Hash that is using "Imadjust" filter I together is proposed. This paper has achieved a satisfactory performance level on histogram equalization as well as geometric deformation.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.96-108
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• 2014

In case of contrast of the image enhancement by using the conventional histogram equalization, over-enhancement, false contouring and distortion such as the details disappearance of the image occurs due to the excessive brightness change. Especially, these distortion appears when the brightness distribution is concentrated in a particular brightness level. In order to solve these problems, improved histogram equalization methods to transform the input histogram by clipping using threshold have been proposed, but contrast enhancement effect is reduced because it does not consider the characteristics of the input image's histogram to apply the same threshold for the entire histogram, and unnatural image is obtained because it does not retain the characteristics of the image. In this paper, to solve the problems of existing methods, we propose new equalization method that suppress excessive brightness changes by applying to the differential compression according to the histogram frequency, and maintain the characteristics of the input image. In addition, we propose a more effectively method to improve contrast by controlling the strength of the compression ratio depending on the characteristics of the input image.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4475-4481
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• 2014

A histogram equalization method have been used traditionally for the image enhancement of low quality images. This uses the transformation function, which is a cumulative density function of an input image, and it has mathematically maximum entropy. This method, however, may yield whitening artifacts. This paper proposes the weighted histogram equalization method based on histogram equalization. It has Weber-Fechner's law for a human's vision characteristics, and a dynamic range modification to solve the problem of some methods, which yield a transformation function, regardless of the input image. Finally, the proposed transformation function was calculated using the weighted average of Weber-Fechner and the histogram equalization transformation functions in a modified dynamic range. The simulation results showed that the proposed algorithm effectively enhances the contrast in terms of the subjective quality. In addition, the proposed method has similar or higher entropy than the other conventional approaches.

• Journal of Broadcast Engineering
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• v.15 no.3
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• pp.332-345
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• 2010

A unified image enhancement method is proposed for high-resolution image which based on color constancy and histogram equalization using edge region. To speed up the method, smaller image is used when parameters of color constancy and histogram equalization are determined. In the color constancy process, nth-derivative of gaussian is applied to x and y axis separately in order to estimate the color of the illumination rapidly. In the histogram equalization process, the histogram obtained from near-edge region is used for the histogram equalization. In the experiments, high-resolution images taken by digital camcorder are used for verifying the performance of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.633-654
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• 2010

High quality camera images, with good contrast and intensity, are needed to obtain the desired information. Images need to be enhanced when they are dark or bright. The histogram equalization technique, which flattens the density distribution of an image, has been widely used to enhance image contrast due to its effectiveness and simplicity. This technique, however, cannot be used to enhance images that are either too dark or too bright. In addition, it is difficult to perform histogram equalization in real-time using a general-purpose computer. This paper proposes a histogram equalization technique with AGC (Automatic Gain Control) to extend the image enhancement range. It is designed using VHDL (VHSIC Hardware Description Language) to enhance images in real-time. The system is implemented with an FPGA (Field Programmable Gate Array). An image processing system with this FPGA is implemented. The performance of this image processing system is measured.

• ETRI Journal
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• v.28 no.4
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• pp.502-505
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• 2006

A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating the acoustic mismatch between training and test environments, but also at reducing the discrepancy between the phonetic distributions of training and test speech data. The algorithm utilizes multiple class-specific reference and test cumulative distribution functions, classifies the noisy test features into their corresponding classes, and equalizes the features by using their corresponding class-specific reference and test distributions. Experiments on the Aurora 2 database proved the effectiveness of the proposed method by reducing relative errors by 18.74%, 17.52%, and 23.45% over the conventional histogram equalization method and by 59.43%, 66.00%, and 50.50% over mel-cepstral-based features for test sets A, B, and C, respectively.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1603-1613
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• 1995

The goal of enhancement is to improve the perceptual aspect and visual appearance of images for human viewers. The objectives of image enhancement vary according to its specific application and an image enhancement algorithms used for a specific objective may not be accepted in some other applications. In this paper we review some of conventional enhancement techniques, such as global histogram equalization(GHE), local histogram equalization(LHE), clipped histogram equalization(CHE). We also describe some modified version of these algorithms. The proposed method is to detect detail information. We distinquish edge from nonedge and apply histigram equalization respectively. Simulation results demonstrate the performance of the proposed method for medical image.