• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1886-1891
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• 2017

Denoising which is one of major research topics in the image processing deals with recovering the noisy images. Natural images are well known not only for their local but also nonlocal similarity. Patterns of unique edges and texture which are crucial for understanding the image are repeated over the nonlocal region. In this paper, a nonlocal similarity based denoising algorithm is proposed. First for every blocks of the noisy image, nonlocal similar blocks are gathered to construct a overcomplete data set which are inherently sparse in the transform domain due to the characteristics of the images. Then, the sparse transform coefficients are filtered to suppress the non-sparse additive noise. Finally, the image is recovered by aggregating the overcomplete estimates of each pixel. Performance experiments with several images show that the proposed algorithm outperforms the conventional methods in removing the additive Gaussian noise effectively while preserving the image details.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.43-50
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• 2022

In this paper, it is shown how the performance of the monopulse algorithm in additive noise is evaluated. In the previous study, the performance analysis of the amplitude-comparison monopulse algorithm was conducted via the first-order and second-order Taylor expansion of four variables. By defining two new random variables from the four variables, it is shown that computational complexity associated with two random variables is much smaller than that associated with four random variables. Performance in terms of mean square error is analyzed from Monte-Carlo simulation. The scheme proposed in this paper is more efficient than that suggested in the previous study in terms of computational complexity. The expressions derived in this study can be utilized in getting analytic expressions of the mean square errors.

• The Journal of the Acoustical Society of Korea
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• v.13 no.3
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• pp.16-24
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• 1994

A single channel adaptive noise canceling (ANC) technique is presented for removing effects of additive noise on the speech signal. The conventional method obtains a reference signal using the pitch estimated on a frame basis from the input speech. The proposed method, however, gets the reference signal using the delay estimated recursively on a sample by sample basis. To estimate the delay, we derive recursion formula of autocorrelation function and average magnitude difference function. The performance of the proposed method is evaluated for the speech signals distorted by the additive white Gaussian noise. Experimental results with normalized least mean square (NLMS) adaptive algorithm demonstrate that the proposed method improves the perceived speech quality quite well besides the signal-to-noise ratio.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.303-307
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• 2003

In this paper, the W(ultra widebend) system is used for high speed transmission applying BPSK(Binary Phase Shift Keying) and QPSK (Quadrature Phase Shift Keying), and utilizing the convolution coding with code rate, 1/2 and constraint length, K=7 in order to reduce the bit error rate. And the performance of system is analyzed in the AWGN(Additive White Gaussian Noise) channel environment by using the Viterbi decoding algorithm and adopting the time-hopping sequence as a multiple access method in order to avoid the multiuser interference.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1518-1523
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• 2012

Due to development of visual media in various industrial sectors, the importance of image processing is increasing. Among the various image processing areas, edge detection is utilized widely for various fields such as object recognition, object segmentation, the medical and other industries. Edge includes the critical factors of images like size, direction and location. Then conventional methods such as Sobel, Prewitt, Roberts and Laplacian are proposed to detect edge. However, edge detection property of these methods is declined when they are applied to the image which corrupted by AWGN(Additive White Gaussian Noise). Therefore, an algorithm using modified weighted filter is proposed in this paper and our method has excellent property on edge detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.684-686
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• 2017

Currently, image processing is used in various fields including military, medical and industrial fields. Noise added to images undermine the quality of images. As such, the removal of noise is an essential step to process images such as through recognition of images, detection of edge and segmentation of images. Studies on removing noise from images are actively being undertaken. One of the leading noises that are added to images is the AWGN(additive white Gaussian noise). This paper suggests an algorithm that synthesizes a filter that uses edge detection and standard deviation to ease AWGN.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.956-958
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• 2016

Currently, image processing has been widely utilized and the noise may be occurred in the processes of image data transmission, processing, and storage. The studies have been actively conducted to eliminate the added noise in the image. The types of noise in the image are various depending on the causes and the forms, and additive white Gaussian noise(AWGN) is the representative one. The algorithm to apply and process the weighted value was suggested by the directions of the pixel in the local mask using edge detection to relieve the added AWGN in the image in this article.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• Transactions of the Korean hydrogen and new energy society
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• v.27 no.3
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• pp.318-327
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• 2016

In order to improve the performance of temperature field measurements by CT-TDLAS (Computer Tomography Tunable Diode Laser Absorption Spectroscopy), a new reconstruction algorithm, named two-ratios-of-three-peaks method is proposed in this paper. Further, two methods for selecting appropriate initial values of the iterative calculation of CT-TDLAS are proposed. One is MLOS (multiplicative line of sight) method and the other one is ALOS (additive line of sight) method. Two-ratios-of-three-peaks (2R3P) algorithm combined with MART (multiplicative algebraic reconstruction technique) is finally developed for the enhancements of reconstructive calculations. The results have been compared with those obtained by the conventional one-ratio-of-two-peaks (1R2P) algorithm. In order to evaluate the performance of this algorithm, numerical test has been performed using phantom Gaussian temperature distributions with $11{\times}11$ square mesh. The performance of the constructed algorithm has been demonstrated by comparing the results obtained in actual burner experiments with those obtained by thermocouples. It has been verified that 2R3P algorithm with MART and MLOS showed best performance than that of 1R2P algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.518-523
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• 2017

This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.