• Journal of Korea Multimedia Society
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• v.18 no.6
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• pp.691-700
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• 2015

Here, we present a new framework for histogram equalization in which both local and global contrasts are enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Filters are chosen depending on image properties, such as noise removal and smoothing. Our experimental results confirmed that this does not increase the computational cost because the filtering process is done by our proposed arrangement of making the histogram while checking neighborhood metrics simultaneously. If the two methods, i.e., histogram equalization and filtering, are performed sequentially, the first method uses the original image data and next method uses the data altered by the first. With combined histogram equalization and filtering, the original data can be used for both methods. The proposed method is fully automated and any spatial neighborhood filter type and size can be used. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

• Journal of Broadcast Engineering
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• v.19 no.4
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• pp.453-467
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• 2014

A typical histogram equalization contrast enhancement effect for improving the image quality is excellent. However, because it appears that excessive changes of the brightness values, The average brightness of the image is changing in units of frames of applications such as a TV video is unsuitable. In order to solve these drawbacks, a modified method of histogram equalization on various studies have been made. But the result images of existing methods sometimes shown visual degradations such as over-enhancement and false contouring. In this paper, we propose improved contrast enhancement method through bi-histogram equalization using target mean brightness based on differential compression method. The proposed method is based on the average brightness value by dividing the histogram, the histogram for each zone, according to the frequency differential of compression. And equalize the modified histogram based on target mean brightness. This allows to suppress deterioration of picture quality, and changes in the average brightness of each frame of video, while maintaining and improving the contrast. Experimental results show that the proposed method compared to the conventional method, the average brightness of each frame from a movie well maintained, and no degradation of the image quality showed a good effect to improve the contrast.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.283-286
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• 2014

X-ray 영상의 경우 검은 배경으로 인해 기존의 히스토그램 평활화를 사용하여 대비비를 향상 시킬 경우 문제가 발생한다. 전역 히스토그램 평활화의 경우 영상의 특징을 고려하지 않은 채 전체적으로 히스토그램 평활화가 이루어지기 때문에 부분적인 명암값을 개선시키기 어렵다. BBHE(Bright Preserving Bi-Histogram Equalization)과 DSIHE(Dualistic Sub-Image Histogram Equalization)과 같은 영역별 히스토그램 평활화의 경우 X-ray 사진특성상 검은 배경으로 인하여 히스토그램 평활화를 적용해도 원하는 대비비를 얻기 힘들며 부분적으로 왜곡이 발생한다. 이러한 문제를 해결하기 위해 본 논문에서는 영상의 히스토그램을 통해 배경 정보를 파악하여 밝기 영역을 나눈 후 히스토그램 평활화를 진행함으로써 X-ray 사진의 대비비를 효율적으로 향상시킨다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.269-272
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• 2005

The principal objective of enhancement methods is to process an image so that the result is more suitable than the original image for a specific application. Images taken in the night can be low-contrast images because of poor environments. In this paper, we compare the structure of ICECA(Image Contrast Enhancement technique using Clustering Algorithm) with the structures of HE(Histogram Equalization), BBHE(Brightness preserving Bi-Histogram Equalization), and Multi -Scale Retinex(MSR). We compared performances of image enhancement methods by applying these methods to a set of diverse images.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.1 s.24
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• pp.13-19
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• 2003

In this paper we discussed the processing procedures of histogram equalization(HE) method and brightness preserving bi-histogram equalization(BBHE) method in the contrast enhancement methods for the performance comparison. With remotely sensed image data of Landsat TM we compared the performances of three methods of Min-Max method, HE method, BBHE method. The experimental results demonstrate that the HE method and BBHE method are more effective in the contrast enhancement performances than the Min-Max method. In the HE method the mean brightness of the resultant output images approached to the middle gray level with regardless of input image mean. In the BBHE method, it is capable of preserving the mean brightness of a original image compared to the HE method while enhancing the contrast of original image effectively. Thus BBHE method is provided more natural enhancement effect than the HE method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.467-472
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• 2005

The principal objective of enhancement methods is to process an image so that the output image is more suitable than the original image lot a specific application. Images taken in the night can be low-contrast images because of poor environments. In this paper, we compared the performance of Image Contrast Enhancement Technique Using Clustering Algorithm(ICECA) with those of color adjustment methods such as Histogram Equalization(HE), Brightness Preserving Bi-Histogram Equalization(BBHE), and the Multi-Scale Refiner(MSR). We compared these methods by applying the image enhancement methods to a set of diverse images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1904-1926
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• 2016

Iris recognition for biometric personnel identification has gained much interest owing to the increasing concern with security today. The image quality plays a major role in the performance of iris recognition systems. When capturing an iris image under uncontrolled conditions and dealing with non-cooperative people, the chance of getting non-ideal images is very high owing to poor focus, off-angle, noise, motion blur, occlusion of eyelashes and eyelids, and wearing glasses. In order to improve the accuracy of iris recognition while dealing with non-ideal iris images, we propose a novel algorithm that improves the quality of degraded iris images. First, the iris image is localized properly to obtain accurate iris boundary detection, and then the iris image is normalized to obtain a fixed size. Second, the valid region (iris region) is extracted from the segmented iris image to obtain only the iris region. Third, to get a well-distributed texture image, bilinear interpolation is used on the segmented valid iris gray image. Using contrast-limited adaptive histogram equalization (CLAHE) enhances the low contrast of the resulting interpolated image. The results of CLAHE are further improved by stretching the maximum and minimum values to 0-255 by using histogram-stretching technique. The gray texture information is extracted by 1D Gabor filters while the Hamming distance technique is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. Results of the proposed method outperformed other methods in terms of equal error rate (EER).