• Journal of Broadcast Engineering
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• v.15 no.3
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• pp.332-345
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• 2010

A unified image enhancement method is proposed for high-resolution image which based on color constancy and histogram equalization using edge region. To speed up the method, smaller image is used when parameters of color constancy and histogram equalization are determined. In the color constancy process, nth-derivative of gaussian is applied to x and y axis separately in order to estimate the color of the illumination rapidly. In the histogram equalization process, the histogram obtained from near-edge region is used for the histogram equalization. In the experiments, high-resolution images taken by digital camcorder are used for verifying the performance of the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.231-236
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• 2014

The transit time of contrast agents and the parameters of time-intensity curves in ultrasonography are important factors to diagnose various diseases of a digestive organ. We have implemented an automatic parametric imaging method to overcome the difficulty of the diagnosis by naked eyes. However, the micro-bubble noise and the respiratory motions may degrade the reliability of the parameter images. In this paper, we introduce an optimization technique based on MRF(Markov Random Field) model to enhance the quality of the parameter images, and present an image tracking algorithm to compensate the image distortion by respiratory motions. A method to extract the respiration periods from the ultrasound image sequence has been developed. We have implemented the ROI(Region of Interest) tracking algorithm using the dynamic weights and a momentum factor based on these periods. An energy function is defined for the Gibbs sampler of the image enhancement method. Through the experiments using the data to diagnose liver lesions, we have shown that the proposed method improves the quality of the parametric images.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1584-1587
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• 2005

Although the picture quality of today's displays is very good already, a continuous improvement is desirable as the new larger display sizes increase the visibility of artifacts. A contributing factor for picture quality enhancement through smart video processing and algorithm design is the information gathered from video statistics. Interesting parameters gathered from video statistics are e.g. the image- and display load, the usage of the color gamut, the estimated power consumption and the occurrence of static image parts. Examples of applications that can benefit from video statistics are power calculations, color gamut mapping algorithms, dynamic backlight control for LCD panels and LED backlights for LCD panels.

• Proceedings of the KSRS Conference
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• v.2
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• pp.736-739
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• 2006

While hyperspectral data are very rich in information, their processing poses several challenges such as computational requirements, noise removal and relevant information extraction. In this paper, the application of advanced scale-space filtering to selected hyperspectral bands was investigated. In particular, a pre-processing tool, consisting of anisotropic diffusion and morphological leveling filtering, has been developed, aiming to an edge-preserving smoothing and simplification of hyperspectral data, procedures which are of fundamental importance during feature extraction and object detection. Two scale space parameters define the extent of image smoothing (anisotropic diffusion iterations) and image simplification (scale of morphological levelings). Experimental results demonstrated the effectiveness of the developed scale space filtering for the enhancement and smoothing of hyperspectral remote sensing data and their advantage against watershed over-segmentation problems and edge detection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.621-628
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• 2021

This paper proposes a method to calibrate a thermal camera with three different perspectives. In particular, the intrinsic parameters of the camera and re-projection errors were provided to quantify the accuracy of the calibration result. Three lenses of the camera capture the same image, but they are not overlapped, and the image resolution is worse than the one captured by the RGB camera. In computer vision, camera calibration is one of the most important and fundamental tasks to calculate the distance between camera (s) and a target object or the three-dimensional (3D) coordinates of a point in a 3D object. Once calibration is complete, the intrinsic and the extrinsic parameters of the camera(s) are provided. The intrinsic parameters are composed of the focal length, skewness factor, and principal points, and the extrinsic parameters are composed of the relative rotation and translation of the camera(s). This study estimated the intrinsic parameters of thermal cameras that have three lenses of different perspectives. In particular, image enhancement based on a deep learning algorithm was carried out to improve the quality of the calibration results. Experimental results are provided to substantiate the proposed method.

• 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.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.685-688
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• 2000

In this paper, we used image information for the enhancement of Korean numeral speech recognition. First, a noisy environment was made by Gaussian generator at each 10 dB level and the generated signal was added to original Korean numeral speech. And then, the speech was analyzed to recognize Korean numeral speech. Speech through microphone was pre-emphasized with 0.95, Hamming window, autocorrelation and LPC analysis was used. Second, the image obtained by camera, was converted to gray level, autocorrelated, and analyzed using LPC algorithm, to which was applied in speech analysis, Finally, the Korean numerial speech recognition with image information was more ehnanced than speech-only, especially in ‘3’, ‘5’and ‘9’. As the same LPC algorithm and simple image management was used, additional computation a1gorithm like a filtering was not used, a total speech recognition algorithm was made simple.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1378-1387
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• 2013

Histogram equalization, which stretches the dynamic range of intensity, is the most common method for enhancing the contrast of image. Contrast limited adaptive histogram equalization(CLAHE), proposed by K. Zuierveld, has two key parameters: block size and clip limit. These parameters mainly control image quality, but have been heuristically determined by user. In this paper, we propose a novel method of determining two parameters of CLAHE using entropy of image. The key idea is based on the characteristics of entropy curves: clip limit vs entropy and block size vs entropy. Clip limit and block size are determined at the point with maximum curvature on entropy curve. Experimental results show that the proposed method improves images with very low contrast.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.790-809
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• 2019

The design of an efficient scaler to enhance the edge data is one of the most important issues in video signal applications, because the perceptual quality of the processed image is sensitively affected by the degradation of edge data. Various conventional scaling schemes have been proposed to enhance the edge data. In this paper, we propose an efficient scaling algorithm for this purpose. The proposed method is based on the discrete cosine transform-based interpolation filter (DCT-IF) because it outperforms other scaling algorithms in various configurations. The proposed DCT-IF incorporates weighting parameters that are optimized for training data. Simulation results show that the quality of the resized image produced by the proposed DCT-IF is much higher than that of those produced by the conventional schemes, although the proposed DCT-IF is more complex than other conventional scaling algorithms.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.7
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• pp.297-300
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• 2015

In medical ultrasonography, transit time and contrast enhancement patterns are considered as important parameters to analyze liver diseases. In many recent researches, time-intensity curves(TIC) have been used for calculating the transit time of the contrast agents. However, the intensity curve may include the variations which are caused by the micro-bubble effect of contrast agents. In this paper, we propose a complementary approach to diagnostic parameter extraction which utilizes a density information as well as the intensity data. The proposed technique improves the accuracy in extraction of the transit time and velocity of contrast agents for detection and characterization of focal liver lesions. Through the experiments using a set of clinical data, we show that the proposed methods can improve the reliability of the parametric image data.