• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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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.

• 전자공학회논문지
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• 제51권3호
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• pp.105-111
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• 2014

영상 해상도 개선은 저해상도 획득 영상의 해상도를 개선하여 고해상도 영상을 생성하는 기술이다. 영상 해상도 개선을 위해서는 저해상도 획득 영상의 열화 과정에서 발생하는 손실된 화소 정보를 정확하게 추정하는 것이 중요하다. 따라서 본 논문에서는 영상 해상도 개선을 위한 다중 부족분 추정 방법을 제안한다. 제안하는 방법은 획득 영상의 부영상 집합에 알려진 열화 및 복원 과정을 수행하여 서로 다른 형태의 다중 부족분을 추정하고, 추정된 부족분과 획득 영상의 보간 영상의 결합을 통해서 결과 영상을 생성하고, 디블러링을 수행하여 최종 복원 영상을 생성한다. 객관적 화질 측정 지표인 PSNR, SSIM, FSIM으로 비교한 결과 제안한 방법이 보간만을 사용하는 방법들보다 높은 값을 가지는 것을 확인하였다. 또한 결과 영상의 시각적 비교 결과 주관적 관점의 화질도 가장 뛰어난 것을 알 수 있었고, 보간만을 사용하는 방법들보다 빠른 계산시간을 가지는 것을 확인할 수 있었다. 제안하는 방법은 영상 해상도 개선을 위한 응용 환경에서 유용하게 사용될 수 있다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.521-524
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• 1999

In spite of advances in image resolution and film contrast, check screen/film mammography remains one of diagnostic imaging modality where the image interpretation is very difficult. For the enhancement of film mammography, in this paper, dyadic wavelet transform is introduced. An unsharp masking technique is proposed and performed in wavelet domain. In addition, simple nonlinear enhancement and a denosing stage that preserves edges using wavelet shrinkage are computed into this technique. In this paper. we propose a new method for the gain setting of nonlinear enhancement and show result and comparison.

• 한국의류학회지
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• 제38권6호
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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.

• JSTS:Journal of Semiconductor Technology and Science
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• 제15권1호
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• pp.60-67
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• 2015

This paper proposes an image-dependent color image enhancement method that uses adaptive luminance enhancement and color emphasis. It effectively enhances details of low-light regions while maintaining well-balanced luminance and color information. To compare the structure similarity and naturalness, we used the tone mapped image quality index (TMQI). The proposed method maintained better structure similarity in the enhanced image than did the space-variant luminance map (SVLM) method or the adaptive and integrated neighborhood dependent approach for nonlinear enhancement (AINDANE). The proposed method required the smallest computation time among the three algorithms. The proposed method can be easily implemented using the field-programmable gate array (FPGA), with low hardware resources and with better performance in terms of similarity.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2009년도 9th International Meeting on Information Display
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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.

• 반도체디스플레이기술학회지
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• 제17권1호
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• pp.71-75
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• 2018

Image enhancement is a necessary end essential step after taking a picture with a digital camera. Many different photo software packages attempt to automate this process with various auto enhancement techniques. This paper provides and implements a system that can learn a user's preferences and apply the preferences into the process of image enhancement. Five major components are applied to the implemented system, which are computing a distance metric, finding a training set, finding an optimal parameter set, training and finally enhancing the input image. To estimate the validity of the method, we carried out user studies, and the fact that the implemented system was preferred over the method without learning user preferences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권6호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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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.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권8호
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• pp.360-367
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• 2002

In this paper, genetic algorithm based adaptive image enhancement filtering scheme is proposed and Implemented on FPGA board. Conventional filtering methods require a priori noise information for image enhancement. In general, if a priori information of noise is not available, heuristic intuition or time consuming recursive calculations are required for image enhancement. Contrary to the conventional filtering methods, the proposed filter system can find optimal combination of filters as well as their sequent order and parameter values adaptively to unknown noise types using structured genetic algorithms. The proposed image enhancement filter system is mainly composed of two blocks. The first block consists of genetic algorithm part and fitness evaluation part. And the second block consists of four types of filters. The first block (genetic algorithms and fitness evaluation blocks) is implemented on host computer using C code, and the second block is implemented on re-configurabe FPGA board. For gray scale control, smoothing and deblurring, four types of filters(median filter, histogram equalization filter, local enhancement filter, and 2D FIR filter) are implemented on FPGA. For evaluation, three types of noises are used and experimental results show that the Proposed scheme can generate optimal set of filters adaptively without a pioi noise information.