• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.8
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• pp.1782-1790
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• 2011

The goal of image processing 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 techniques 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 histogram shrink, equalization, and the conservative adaptive contrast enhancement filter. And also, The adaptive contrast enhancement filter has optimized the applied values of variables which is configured rightly the medical image field. In the postprocessing, we used the histogram equalization method. As a result, the inputs which used a medical images has enhanced the edges of a result images which has applied the proposed filter. And also, because of the postprocessing, the image outlines has been lightened.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2287-2312
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• 2018

Extracting key visual information from images containing natural scene is a challenging task and an important step for the visually impaired to recognize information based on tactile graphics. In this study, a novel method is proposed for extracting salient regions based on global contrast enhancement and saliency cuts in order to improve the process of recognizing images for the visually impaired. To accomplish this, an image enhancement technique is applied to natural scene images, and a saliency map is acquired to measure the color contrast of homogeneous regions against other areas of the image. The saliency maps also help automatic salient region extraction, referred to as saliency cuts, and assist in obtaining a binary mask of high quality. Finally, outer boundaries and inner edges are detected in images with natural scene to identify edges that are visually significant. Experimental results indicate that the method we propose in this paper extracts salient objects effectively and achieves remarkable performance compared to conventional methods. Our method offers benefits in extracting salient objects and generating simple but important edges from images containing natural scene and for providing information to the visually impaired.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.59-62
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• 2018

In this paper, we propose a contrast enhancement algorithm using guided image filter (GIF). The GIF is used to divide an HDR image into a base layer and a detail layer. The energy scale of base layer determinate the darkness and brightness of the image. However, the detail information in the base layer is difficult to be displayed because of the high brightness and clusters of low brightness. We propose a contrast enhancement method by adjusting the gray level of base layer by subtracting the mean value of itself. It is combined with the detail layer to preserve the detail information. Experiment results show that the proposed algorithm has better performance in detail preservation and contrast enhancement.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1117-1124
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• 2018

Contrast enhancement plays an important role in various computer vision systems, since their usability can be improved with visibility enhancement of the images affected by weather and lighting conditions. This paper introduces a histogram modification algorithm that reflects the properties of original images in order to eliminate the saturation effect and washed-out of image details due to the over-enhancement. Our method modifies the original histogram so that an additive term fill histogram pits and the gamma correction suppresses histogram spikes. The parameters for the additive term and gamma correction are adjusted automatically according to statistical properties of the images. Experimental results for various low contrast and hazy images demonstrate that the proposed contrast enhancement improves visibility and reduces haze components effectively, while preserving the characteristics of original images, than the conventional methods.

• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1205-1211
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• 2012

Recently, the evaluating method of the bone mineral density (BMD) in X-ray absorptiometry image has been studied for the early diagnosis of osteoporosis which is known as a metabolic disease. The BMD, in general, is evaluated by calculating pixel intensity in the bone segmented regions. Accurate bone region extraction is extremely crucial for the BMD evaluation. So, a X-Ray image enhancement is needed to get precise bone segmentation. In this paper, we propose an image enhancement method of X-ray image having multiple noise based sparse representation. To evaluate the performance of proposed method, we employ the contrast to noise ratio (CNR) metric and cut-view graphs visualizing image enhancement performance. Experimental results show that the proposed method outperforms the BayesShrink noise reduction methods and the previous noise reduction method in sparse representation with general noise model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.958-964
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• 2010

Histogram Equalization(HE) is one of the well known methods for contrast enhancement. but, it did not applied directly due to side effects such as significant change in brightness or washed out appearance. Many conventional method try to overcome this problem but they did not guarantee various image or depend on user define parameter. In this paper, an Adaptive histogram Compensated Histogram Equalization(ACHE) is proposed for contrast enhancement. ACHE has a parameter that based on median of input image. Histogram of input image is compensated according to parameter. And then finally compensated histogram is equalized. Experimental results show that proposed method suppresses side effects such as detail loss or washed out appearance. Moreover, parameter calculated automatically with low computation complexity. As a result, it could applies FPD directly.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.263-270
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• 2009

Histogram Equalization (HE) is a very popular technique for enhancing the contrast of an image. HE stretches the dynamic range of an image using the cumulative distribution function of a given input image, therefore improving its contrast. However, HE has a well-known problem : when HE is applied for the contrast enhancement, there is a significant change in brightness. To resolve this problem, we propose An Adaptive Contrast Enhancement Algorithm using Subhistogram Area-Ratioed Histogram Redistribution, a new method that helps reduce excessive contrast enhancement. This proposed algorithm redistributes the dynamic range of an input image using its mean luminance value and the ratio of sub-histogram area. Experimental results show that by this redistribution, the significant change in brightness is reduced effectively and the output image is able to preserve the naturalness of an original image even if it has a poor histogram distribution.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.1-6
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• 2016

In this paper, we propose an image enhancement method for vehicle recorder by dividing the images into sub-images and finding local histograms of the sub-images. The proposed method includes the following steps. Firstly, the input image is divided into ($N{\times}M$) pieces. And the sub-images are used to make groups using the adjacent piece-images (eg. piece-imagei,j, piece-imagei,j+1, piece-imagei+1,j and piece-imagei+1,j+1). Secondly, the contrast enhancement processes are executed using the local histogram of the sub-images. Finally, overall image is reconstructed by using a transfer function that reflects the characteristics of the sub-image. The proposed method might achieve more enhanced images for vehicle recorder by suppressing excessive image contrast.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.369-370
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• 2007

In this paper, an image enhancement method by saturation and contrast improvement is proposed. Histogram equalization with color difference makes higher contrast. By generating saturation amplification ratio with color difference, the saturation improves effectively. The experimental results show that the proposed algorithm has higher contrast and more natural - look than the conventional methods.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.4
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• pp.840-866
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• 2014

Visibility in underwater images is usually poor because of the attenuation of light in the water that causes low contrast and color variation. In this paper, a new approach for underwater image quality improvement is presented. The proposed method aims to improve underwater image contrast, increase image details, and reduce noise by applying a new method of using contrast stretching to produce two different images with different contrasts. The proposed method integrates the modification of the image histogram in two main color models, RGB and HSV. The histograms of the color channel in the RGB color model are modified and remapped to follow the Rayleigh distribution within certain ranges. The image is then converted to the HSV color model, and the S and V components are modified within a certain limit. Qualitative and quantitative analyses indicate that the proposed method outperforms other state-of-the-art methods in terms of contrast, details, and noise reduction. The image color also shows much improvement.