• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.215-218
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• 2013

In this paper, we proposed a method for enhancement of color images with sky regions. The input image is converted into HSV space and then sky and non-sky regions are separated. For sky region, saturation enhancement is performed for each pixel based on the enhancement factor calculated from the average saturation of its local neighborhood. On the other hand, for the non-sky region, the enhancement is applied only on the luminance value (V) component of the HSV color image, which is performed in two steps. The luminance enhancement, which is also called as dynamic range compression, is carried out using nonlinear transfer function. Again, each pixel is further enhanced for the adjustment of the image contrast depending upon the center pixel and its neighborhood pixel values. At last, the original H and V component image and enhanced S component image for the sky region, and original H and S component image and enhanced V component image for the non-sky region are converted back to RGB image.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1233-1243
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• 2015

The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms.

• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.6
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• pp.810-822
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• 2014

This study investigated the effects of sociocultural influence and body image on appearance enhancement behavior (facial management, clothing selection, and weight/figure management). For data collection, a questionnaire was administrated to 378 female college students in Seoul and Gyeonggi-do from May $23^{rd}$ to June $10^{th}$ 2013. A SPSS 18.0 statistics package was used to analyze data along with descriptive statistical analysis, frequency analysis, factor analysis, reliability analysis, and regression analysis and frequency analysis. The results were as follows. First, sociocultural influences were divided into three factors: media influence, peer influence, and parental influence. Overall sociocultural influences had positive effects on appearance enhancement behavior. Second, body image was divided into: appearances-management, body-satisfaction and body confidence. Sociocultural influences had a significant effect on overall body image. Third, body image positively affected overall appearance enhancement behavior.

• ETRI Journal
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• v.39 no.1
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• pp.76-86
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• 2017

In this study, we propose an automatic contrast enhancement method based on transfer function modification (TFM) by histogram equalization. Previous histogram-based global contrast enhancement techniques employ histogram modification, whereas we propose a direct TFM technique that considers the mean brightness of an image during contrast enhancement. The mean point shifting method using a transfer function is proposed to preserve the mean brightness of an image. In addition, the linearization of transfer function technique, which has a histogram flattening effect, is designed to reduce visual artifacts. An attenuation factor is automatically determined using the maximum value of the probability density function in an image to control its rate of contrast. A new quantitative measurement method called sparsity of a histogram is proposed to obtain a better objective comparison relative to previous global contrast enhancement methods. According to our experimental results, we demonstrated the performance of our proposed method based on generalized measures and the newly proposed measurement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3188-3207
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• 2017

In this paper, we propose an image enhancement algorithm to improve license plate extraction rate in various environments (Day Street, Night Street, Underground parking lot, etc.). The proposed algorithm is composed of image enhancement algorithm and license plate extraction algorithm. The image enhancement method can improve an image quality of the degraded image, which utilizes a histogram information and overall gray level distribution of an image. The proposed algorithm employs an interpolated probability distribution value (PDV) in order to control a sudden change in image brightness. Probability distribution value can be calculated using cumulative distribution function (CDF) and probability density function (PDF) of the captured image, whose values are achieved by brightness distribution of the captured image. Also, by adjusting the image enhancement factor of each part region based on image pixel information, it provides a function that can adjust the gradation of the image in more details. This processed gray image is converted into a binary image, which fuses narrow breaks and long thin gulfs, eliminates small holes, and fills gaps in the contour by using morphology operations. Then license plate region is detected based on aspect ratio and license plate size of the bound box drawn on connected license plate areas. The images have been captured by using a video camera or a personal image recorder installed in front of the cars. The captured images have included several license plates on multilane roads. Simulation has been executed using OpenCV and MATLAB. The results show that the extraction success rate is more improved than the conventional algorithms.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.5
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• pp.549-561
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• 2012

This study investigates the effects of sociocultural influences and body image on appearance enhancement behaviors (facial management, clothing selection, and weight/figure management). For data collection, a questionnaire was administrated to 562 female college students in Gwangju City, Chonnam area and Chonbuk area, Korea, from May 23 to June 10, 2011. To analyze the data, a SPSS 18.0 statistics package was used, and descriptive statistical analysis, frequency analysis, factor analysis, reliability analysis, and regression analysis were conducted. The results were as follows. First, sociocultural influences were divided into three factors: parental influence, media influence, and peer influence. Overall sociocultural influences had positive effects on appearance enhancement behavior. Second, body image was divided into two factors: weight-concern and appearance-concern. Sociocultural influences had significant effects on overall body image. Third, body image appeared to have positive effects on overall appearance enhancement behavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2483-2504
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• 2023

Most existing low-light enhancement algorithms either use a large number of training parameters or lack generalization to real-world scenarios. This paper presents a novel lightweight and robust pixel-wise polynomial approximation-based deep network for low-light image enhancement. For mapping the low-light image to the enhanced image, pixel-wise higher-order polynomials are employed. A deep convolution network is used to estimate the coefficients of these higher-order polynomials. The proposed network uses multiple branches to estimate pixel values based on different receptive fields. With a smaller receptive field, the first branch enhanced local features, the second and third branches focused on medium-level features, and the last branch enhanced global features. The low-light image is downsampled by the factor of 2b-1 (b is the branch number) and fed as input to each branch. After combining the outputs of each branch, the final enhanced image is obtained. A comprehensive evaluation of our proposed network on six publicly available no-reference test datasets shows that it outperforms state-of-the-art methods on both quantitative and qualitative measures.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.51-60
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• 2024

Aiming at the problem of low contrast and difficult to recognize video images in low-light environment, we propose an adaptive contrast compensation enhancement algorithm based on human visual perception. First of all, the video image characteristic factors in low-light environment are extracted: AL (average luminance), ABWF (average bandwidth factor), and the mathematical model of human visual CRC(contrast resolution compensation) is established according to the difference of the original image's grayscale/chromaticity level, and the proportion of the three primary colors of the true color is compensated by the integral, respectively. Then, when the degree of compensation is lower than the bright vision precisely distinguishable difference, the compensation threshold is set to linearly compensate the bright vision to the full bandwidth. Finally, the automatic optimization model of the compensation ratio coefficient is established by combining the subjective image quality evaluation and the image characteristic factor. The experimental test results show that the video image adaptive enhancement algorithm has good enhancement effect, good real-time performance, can effectively mine the dark vision information, and can be widely used in different scenes.

• Journal of the Korean Home Economics Association
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• v.45 no.7
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• pp.131-140
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• 2007

The purpose of this study was to examine the influence of aesthetic surgery attitude, self-esteem and body image on clothing behavior. Subjects were 356 female college students in Seoul. The data obtained were analyzed by reliability analysis, factor analysis, correlation analysis, stepwise multiple regression analysis and t-test. The major results of this study were as follows: First, five factors of aesthetic surgery attitude were identified: risk tolerance of aesthetic surgery, need of aesthetic surgery, image improvement via aesthetic surgery, keeping the secret of aesthetic surgery, and others' expectation of aesthetic surgery. Second, significant relationships were found between body image and clothing behavior, and self-esteem and body-enhancement of clothing. Also, risk tolerance of aesthetic surgery, need of aesthetic surgery, and image improvement via aesthetic surgery had a significant correlation with clothing behavior. Third, the most important variable which affected the aesthetics and body-enhancement of clothing was body image. The entertainer imitation behavior of clothing was influenced by need of aesthetic surgery, body image, keeping the secret of aesthetic surgery, risk tolerance of aesthetic surgery, and image improvement via aesthetic surgery. Preference for luxury goods of clothing was influenced by need of aesthetic surgery and body image. Body-enhancement of clothing was influenced by body image, image improvement via aesthetic surgery, and self-esteem.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2197-2204
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• 2015

In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.