• The korean journal of orthodontics
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• v.32 no.1 s.90
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• pp.59-69
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• 2002

The purpose of this study was to evaluate the reproducibility of lateral cephalometric landmarks according to radiographic image enhancement, and to contribute to the identification of cephalometric landmarks. Lateral cephalograms of ten individuals were taken and stored into computer. The images were then enhanced up to four grades by Quick Ceph Image Pro$^{TM}$ on condition that the gray-scale equalization number was 50 and the detail enhancement number was 50. After thirty two landmarks were identified on monitor images by five observers, the deviations from the mean, the distances estimated between identified points and the mean point of five identified points, were evaluated for each landmark at each enhancement grade. Through the statistical analysis, following results were obtained. 1. In case of unenhanced radiographic images, the inter-observer reproducibility of the landmarks showed a large variation. 2. The comparison of deviation from the mean according to the degree of radiographic image enhancement for each landmark showed that the inter-observer reproducibility was significantly different at 5 landmarks. 3. The landmark of pterygomaxillary fissure showed higher reproducibility at enhancement grade 1 and 2 images than at unenhanced images. So did the landmark of posterior nasal spine at enhancement grade 1 images, and the landmark of menton at enhancement grade 2, 3 and 4 images respectively. The above results suggest that the reproducibility of some landmarks can be increased by radiographic image enhancement during the identification of the lateral cephalometric landmarks on the monitor.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.1 s.149
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• pp.59-70
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• 2006

This study has analyzed females' motives and psychological experiences related to appearance-enhancement seeking behavior(weight control practice and cosmetic surgery). In this study, in-depth interviews were carried out to 11 females who had experienced weight control practices and cosmetic surgery in June 2001. There is social standard in ideal body image. One perceive a physical idea and own body through society(mass media, reference group), others and clothing, and recognize the ideal body and internalize the social standard as own worth. The discrepancies between ideal body image internalized as standards of own worth and real body image became a setup for body dissatisfaction. Increasing in body dissatisfaction, rejection of own body grow, furthermore body is perceived with distortion. In order to remove a negative body image and to reach ideal body image, appearance-enhancement seeking behavior such as weight control and cosmetic surgery is made. By appearance enhancing, one come to closer to ideal image of which one pursuit oneself. Therefore body satisfaction feeling increase, self·esteem rise, manner of life and character change to with affirmation. Otherwise, strengthening of appearance-concern and of appearance enhancement seeking desire has the possibility of developing into bulimia and cosmetic surgery addiction. Also, the standard of beauty in appearance rise by degrees, and that produces motives of appearance-enhancement seeking behavior.

• Journal of the Semiconductor & Display Technology
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• v.14 no.4
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• pp.50-55
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• 2015

This paper proposes a new histogram partition stretching and shrinking method for infrared image enhancement. The proposed method divides the histogram of an input image into three partitions according to its mean value and standard deviation. The method stretches both the dark partition and the bright partition of the histogram, while it shrinks the medium partition. As the result, both the dark part and the bright part of the image have more brightness levels. The proposed method is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiments were conducted by using various infrared images. The results show that the proposed algorithm is successful for the infrared image enhancement.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.1035-1038
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• 2007

In this paper, we propose a color image improvement method. The proposed algorithms are classified with the adaptive contrast stretching method for contrast enhancement and the adaptive saturation enhancement method for saturation enhancement. The adaptive contrast stretching method is to compensate a significant change of brightness while luminance is processed. The adaptive saturation enhancement method inhibits its saturation from de-saturation and oversaturation while chrominance is processed. The proposed algorithms are focused on a preference color processing in order to generate better image quality than the algorithms focused on a uniform color processing for human vision.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.192-195
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• 2003

This paper is a study on the psuedocolor image enhancement of infrared image using B-spline wavelet transform. The psuedocolor enhancement is that the frequency lose on the minimum, the decomposition enhancement is realized by B-spline and RGB image is extracted by wavelet transform. The result of experiment increases enhanced infrared image as 3dB by processing of B-spline and wavelet transform.

• Imaging Science in Dentistry
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• v.44 no.1
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• pp.31-35
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• 2014

Purpose: This study was performed to investigate the effect of image enhancement of periapical radiographs according to the diagnostic task. Materials and Methods: Eighty digital intraoral radiographs were obtained from patients and classified into four groups according to the diagnostic tasks of dental caries, periodontal diseases, periapical lesions, and endodontic files. All images were enhanced differently by using five processing techniques. Three radiologists blindly compared the subjective image quality of the original images and the processed images using a 5-point scale. Results: There were significant differences between the image quality of the processed images and that of the original images (P< 0.01) in all the diagnostic task groups. Processing techniques showed significantly different efficacy according to the diagnostic task (P< 0.01). Conclusion: Image enhancement affects the image quality differently depending on the diagnostic task. And the use of optimal parameters is important for each diagnostic task.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.311-317
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• 2001

The study on Shilla monumental inscription has beer accomplished by many historians. However, the research on enhancement of monumental inscription image using digital image processing technique is not sufficient. The preprocessing using computer is needed fur accurate interpretation of history. In this paper, digital image enhancement algorithm based on mathematical morphology for noise reduction and character clearness is proposed. In the experiment, the subjective image quality is improved using the proposed algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.317-320
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• 2002

In this paper, an automatic histogram specification method is proposed for image enhancement, Fuzzy membership value is adopted for the representation of image histogram. The desired PDF is automatically constructed by the fuzzy membership value. Fuzzy membership value is extracted from dark membership, bright membership function and original histogram. The effectual results are demonstrated by desired PDF which meet the image enhancement requirements. The performance and effectiveness are shown by the analysis and the resultant image in comparison with histogram equalization method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3245-3271
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• 2016

To obtain well-dehazed images at the receiver while sustaining low bit rates in the transmission pipeline, this paper investigates the effects of image dehazing methods using dehazing contrast-enhancement filters on image compression for surveillance systems. At first, this paper proposes a novel image dehazing method by using a new method of calculating the transmission function—namely, the direct denoising method. Next, we deduce the dehazing effects of the direct denoising method and image dehazing method based on dark channel prior (DCP) on image compression in terms of ringing artifacts and blocking artifacts. It can be concluded that the direct denoising method performs better than the DCP method for decompressed (reconstructed) images. We also improve the direct denoising method to obtain more desirable dehazed images with higher contrast, using the saliency map as the guidance image to modify the transmission function. Finally, we adjust the parameters of dehazing contrast-enhancement filters to obtain a corresponding composite peak signal-to-noise ratio (CPSNR) and blind image quality assessment (BIQA) of the decompressed images. Experimental results show that different filters have different effects on image compression. Moreover, our proposed dehazing method can strike a balance between image dehazing and image compression.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.135-145
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• 2020

Recently, deep-learning based methods for low-light image enhancement accomplish great success through supervised learning. However, they still suffer from the lack of sufficient training data due to difficulty of obtaining a large amount of low-/normal-light image pairs in real environments. In this paper, we propose an unsupervised learning approach for single low-light image enhancement using the bright channel prior (BCP), which gives the constraint that the brightest pixel in a small patch is likely to be close to 1. With this prior, pseudo ground-truth is first generated to establish an unsupervised loss function. The proposed enhancement network is then trained using the proposed unsupervised loss function. To the best of our knowledge, this is the first attempt that performs a low-light image enhancement through unsupervised learning. In addition, we introduce a self-attention map for preserving image details and naturalness in the enhanced result. We validate the proposed method on various public datasets, demonstrating that our method achieves competitive performance over state-of-the-arts.