• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.1
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• pp.15-24
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• 2008

This paper proposes a scheme of global image contrast enhancement using local contrast improvement. Methods of global image contrast enhancement redistribute the image gray level distribution using histogram equalization without considering image properties, and cause the result image to include image pixels with excessive brightness. On the other hand, methods of the block-based local image contrast enhancement have blocking artifacts and a problem of eliminating important image features during an image process to reduce them. In order to solve these problems, the proposed method executes the block-based histogram equalization on temporary images that an input image is divided into various fixed-size blocks. And then it performs the global contrast enhancement by applying the global histogram equalization functions to the original input image. Since the proposed method selects the best histogram equalization function from temporary images that are improved by the block-based local image contrast enhancement, it has the advantages of both the local and global image contrast enhancement approaches.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.35-44
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• 2015

The main purpose of image enhancement is to improve certain characteristics of an image to improve its visual quality. This paper proposes a method for image contrast enhancement that can be applied to both medical and natural images. The proposed algorithm is designed to achieve contrast enhancement while also preserving the local image details. To achieve this, the proposed method combines local image contrast preserving dynamic range compression and contrast limited adaptive histogram equalization (CLAHE). Global gain parameters for contrast enhancement are inadequate for preserving local image details. Therefore, in the proposed method, in order to preserve local image details, local contrast enhancement at any pixel position is performed based on the corresponding local gain parameter, which is calculated according to the current pixel neighborhood edge density. Different image quality measures are used for evaluating the performance of the proposed method. Experimental results show that the proposed method provides more information about the image details, which can help facilitate further image analysis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.295-299
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• 2002

This paper presents an image contrast enhancement technique for improving low contrast images. We applied fuzzy logic to develop an image contrast enhancement technique in the viewpoint of considering that the low pictorial information of a low contrast image is due to the vaguness or fuzziness of the multivalued levels of brightness rather than randomness. The fuzzy image contrast enhancement technique consists of three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. In the stage of image fuzzification, we need to select a crossover point. To select the crossover point automatically the K-means algorithm is used. The problem of crossover point selection can be considered as the two-category, object and background, classification problem. The proposed method is applied to an experimental image with 256 gray levels and the result of the proposed method is compared with that of the histogram equalization technique. We used the index of fuzziness as a measure of image quality. The result shows that the proposed method is better than the histogram equalization technique.

• 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.

• Journal of the Optical Society of Korea
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• v.18 no.6
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• pp.809-817
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• 2014

The loss in contrast-discrimination ability of the human visual system under high ambient illumination level can cause image quality degradation in mobile phones. In this paper, we propose a perceived contrast compensation method by processing the original displayed image. With consideration that the perceived contrast significantly varies across the image, this method extracts the local band contrast from the original image; it then compensates these contrast components to counteract the perceived contrast degradation. Experimental results demonstrate that this method can maintain most contrast details even in high ambient illumination levels.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.279-282
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• 2001

This paper presents an image contrast enhancement technique for improving the low contrast images using the improved IAFC(Integrated Adaptive Fuzzy Clustering) Model. The low pictorial information of a low contrast image is due to the vagueness or fuzziness of the multivalued levels of brightness rather than randomness. Fuzzy image processing has three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. Using a new model of automatic crossover point selection, optimal crossover point is selected automatically. The problem of crossover point selection can be considered as the two-category classification problem. The improved MEC can classify the image into two classes with unsupervised teaming rule. The proposed method is applied to some experimental images with 256 gray levels and the results are compared with those of the histogram equalization technique. We utilized the index of fuzziness as a measure of image quality. The results show that the proposed method is better than the histogram equalization technique.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.373-374
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• 2007

This paper proposes a natural contrast enhancement algorithm that preserves the brightness of an image. In the case that an image has the partially dense distribution of intensity, conventional contrast enhancement algorithms degrade the image quality because they excessively change the intensity values. In contrast to convention algorithms, the proposed method is able to obtain a natural image with the high contrast using the concept of brightness preservation. The experimental results show the effectiveness of our algorithm.

• Journal of the Korean Society of Costume
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• v.60 no.1
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• pp.28-42
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• 2010

A purpose of this study is to 6nd out how the casual and formal style clothes of stripe pattern giving variety by pattern direction, pattern width, and contrast coloration have an effect on image of wearers. For this, 192 stimuli were made and 1200 testee evaluated them using semantic differential scale. As a result, five image dimensions were drawn as a factor of attractiveness, gracefulness, activeness, visibility, and tenderness. Unlike the value contrast previously researched, it showed that chroma contrast coloration which was interacted with a color tone contrast coloration had an effect on all the 5 image dimensions. This result was recognized as significant clothes dues in evaluating the image of stripe wearers. Besides, clothing style, stripe pattern, and contrast coloration were made clear as an efficient parameter in image presentation of clothing wearers.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.972-974
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• 2009

This paper presents smart regional contrast enhancement technique of partitioned image for local dimming backlight on small-sized mobile display to reach two goals. One is to save the power consumption, and the other to improve contrast ratio of display image. Recently new advanced method is proposed, named local dimming method, that backlight LED is positioned on backside of the display panel. So it is important to partition an image by sub blocks and then post-processing independantly. This means regional contrast enhancement. After partitioning, we compare the mean luminance(Y) value of each sub-block image with the one of original whole image. If some blocks have the mean value lower than the one of whole image, they are processed with the proposed method and others are bypassed. Simultaneously the information of the processed blocks are transferred to BLC(Backlight LED Controller). And then the supply current of each backlight LED is reduced to realize the contrast ratio enhancement and at the same time to power consumption reduction. In addition, we verify this proposed method is free from blocking artifacts.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.867-871
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• 2004

In this paper, a new analytic approach for shape preserving contrast enhancement is presented. Contrast enhancement is achieved by means of segmental histogram stretching modification which preserves the given image shape, not distorting the original shape. After global stretching, the image is partitioned into several level-sets according to threshold condition. The image information of each level-set is represented as typical value based on grouped differential values. The basic property is modified into common local schemes, thereby introducing the enhanced effect through extreme discrimination between subsets. The scheme is based on stretching the histogram of subsets in which the intensity gray levels between connected pixels are approximately same In spite of histogram widening, stretched by local image information, it neither creates nor destroys the original image, thereby preserving image shape and enhancing the contrast. By designing local histogram stretching operations, we can preserve the original shape of level-sets of the image, and also enhance the global intensity. Thus it can hold the main properties of both global and local image schemes, which leads to versatile applications in the field of digital epigraphy.