• Journal of Korea Multimedia Society
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• v.20 no.3
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• pp.446-455
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• 2017

A color image compression based on saliency map was proposed. The proposed method provides higher quality in saliency blocks on which people's attention focuses, compared with non-saliency blocks on which the attention less focuses at a given bitrate. The proposed method uses 3 different quantization tables according to each block's saliency level. In the experiment using 6 typical images, we compared the proposed method with JPEG and other conventional methods. As the result, it showed that the proposed method (Qup=0.5*Qx) is about 3.1 to 1.2 dB better than JPEG and others in saliency blocks in PSNR at the almost similar bitrate. In the comparison of result images, the proposed one also showed less error than others in saliency blocks.

• Journal of IKEEE
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• v.2 no.2 s.3
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• pp.185-193
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• 1998

Fractal-based monochrome image coding method can be easily applied for color image compression by splitting the color image into different primary spectral channels such as RGB, YIQ or $YC_bC_r$, and encoding each channel independently According to this method, it needs to repeat the fractal coding for each channel, so it have the problem of encoding time. In this paper, a fractal-based coder for color still image is proposed which features the enhancement of compression rate and the reduction of coding time. As the result of the experiment where the proposed algorithm is applied far color images, the compression rate is enhanced by 28 : 1 above with average PSNR value $28{\sim}29[dB]$, do not lossless encoding process using JPEG. And the encoding time is reduced by maximum 11.5 %.

• 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.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3718-3726
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• 1999

Color image is more effective than gray one in human visual perception. Therefore, color image processing becomes important area. Color images are often corrupted by noises due to the input sensor, channel transmission errors and so on. Some filtering techniques such as vector median, mean filter, and vector $\alpha-trimmed$ mean filter have been used for color noise removal. Among them, vector $\alpha-trimmed$ mean filter gave the best performance in the mixed color noise. But, there are edge shift and blurring effect because vector $\alpha-trimmed$ mean filter is uniformly processed across the image. So, we proposed a conditional fuzzy cluster filter to improve this problems. Simulation results showed that the proposed scheme improves the NCD measure and visual quality over the conventional vector $\alpha-trimmed$ mean filter in the mixed color noise.

• Clinical Endoscopy
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• v.56 no.5
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• pp.553-562
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• 2023

Colonoscopy plays an important role in reducing the incidence and mortality of colorectal cancer by detecting adenomas and other precancerous lesions. Image-enhanced endoscopy (IEE) increases lesion visibility by enhancing the microstructure, blood vessels, and mucosal surface color, resulting in the detection of colorectal lesions. In recent years, various IEE techniques have been used in clinical practice, each with its unique characteristics. Numerous studies have reported the effectiveness of IEE in the detection of colorectal lesions. IEEs can be divided into two broad categories according to the nature of the image: images constructed using narrow-band wavelength light, such as narrow-band imaging and blue laser imaging/blue light imaging, or color images based on white light, such as linked color imaging, texture and color enhancement imaging, and i-scan. Conversely, artificial intelligence (AI) systems, such as computer-aided diagnosis systems, have recently been developed to assist endoscopists in detecting colorectal lesions during colonoscopy. To gain a better understanding of the features of each IEE, this review presents the effectiveness of each type of IEE and their combination with AI for colorectal lesion detection by referencing the latest research data.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.29 no.2
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• pp.423-433
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• 1999

Purpose : To assess the methods for the clinical evaluation of the longitudinal bone changes after implantation of tooth ash-plaster mixture into the defect area of human jaws. Materials and methods : Tooth ash-plaster mixtures were implanted into the defects of 8 human jaws. 48 intraoral radiograms taken with copper step wedge as reference at soon, 1st, 2nd, 4th, and 6th week after implantation of mixture were used. X-ray taking was standardized by using Rinn XCP device customized directly to the individual dentition with resin bite block. The images inputted by Quick scanner were digitized and analyzed by NIH image program. Cu­equivalent values were measured at the implanted sites from the periodic digital images. Analysis was performed by the bidirectional subtraction with color enhancement and the surface plot of resliced contiguous image. The obtained results by the two methods were compared with Cu­equivalent value changes. Results : The average determination coefficient of Cu-equivalent equations was 0.9988 and the coefficient of variation of measured Cu values ranged from 0.08~0.10. The coefficient of variation of Cu-equivalent values measured at the areas of the mixture and the bone by the conversion equation ranged from 0.06 ~0.09. The analyzed results by the bidirectional subtraction with color enhancement were coincident with the changes of Cu-equivalent values. The surface plot of the resliced contiguous image showed the three dimensional view of the longitudinal bone changes on one image and also coincident with Cu-equivalent value changes after implantation. Conclusion : The bidirectional subtraction with color enhancement and the surface plot of the resliced contiguous image was very effective and reasonable to analyze clinically and qualitatively the longitudinal bone change. These methods are expected to be applicable to the non-destructive test in other fields.

• Journal of the Korean Society for information Management
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• v.17 no.4
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• pp.157-170
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• 2000

CHROMA. a content-based image rctl-ieval syslem lhas inlroduced a perceptual color modcl, which can siinulatc the visual perceplual process of lhuman beings and eliminate ihe l~roblem ol condilional color variations. Th~s model Ihoweve~ rcgarded shadows and rcllcctions as u n k ~ ~ o u ~ i colors. and took no account of ihe inforclation which can bc gamed from them Th~s ~tudy atlempls Lo estnnale (he unbhown colors using i~ght~ng dil-cclions and lo prove that the process ol unknown colol eslimation can enhance the lperformance o l image retrieval syslems. With the ckperimcntal results, it was concludcd that thc model pmposcd in this study can enhance the perfomancc of content-based image retrieval systems using Lhe ]~ercepiual color model.

• Current Optics and Photonics
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• v.7 no.5
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• pp.485-495
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• 2023

The pixel size in high-resolution complementary metal-oxide-semiconductor (CMOS) image sensors continues to shrink due to chip size limitations. However, the pixel pitch's miniaturization causes deterioration of optical performance. As one solution, a quad color filter (CF) array with pixel binning has been developed to enhance sensitivity. For high sensitivity, the microlens structure also needs to be optimized as the CF arrays change. In this paper, the covered microlens, which consist of four microlenses covered by one large microlens, are proposed for the quad CF array in the backside illumination pixel structure. To evaluate the optical performance, the suggested microlens structure was simulated from 0.5 ㎛ to 1.0 ㎛ pixels at the center and edge of the sensors. Moreover, all pixel structures were compared with and without in-pixel deep trench isolation (DTI), which works to distribute incident light uniformly into each photodiode. The suggested structure was evaluated with an optical simulation using the finite-difference time-domain method for numerical analysis of the optical characteristics. Compared to the conventional microlens, the suggested microlens show 29.1% and 33.9% maximum enhancement of sensitivity at the center and edge of the sensor, respectively. Therefore, the covered microlens demonstrated the highly sensitive image sensor with a quad CF array.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.251-254
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• 2002

Currently many devices reproduce electronic images in a variety of ways. However, the colors that are reproduced are different from the original color due to the differences in the gamut between devices. In this paper, a gamut mapping method utilizing a simultaneous mapping function and a lightness rescaling is proposed. This method enhance the local-color characteristics and lightness contrast. The experimental result shows that the overall contrast and the colorfulness were increased.

• Journal of IKEEE
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• v.25 no.1
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• pp.139-147
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• 2021

This paper introduces an image decomposition-based deep learning method and loss function to improve low-light images. In order to remove color distortion and halo artifact, illuminance channel of an input image is decomposed into reflectance and luminance channels, and a decomposition-based multiple structural deep learning process is applied to each channel. In addition, a mixed norm-based loss function is described to increase the stability and remove blurring in reconstructed image. Experimental results show that the proposed method effectively improve various low-light images.