• Proceedings of the Korea Water Resources Association Conference
• /
• 2006.05a
• /
• pp.1032-1036
• /
• 2006

본 연구에서는 영산강 본류를 대표하는 나주지점을 대상으로 하여, 해당 지점에서 자동 측정되고 있는 수질 항목들 중에서 총유기탄소(TOC: Total Organic Carbon)의 시계열 자료에 대한 비선형 동역학적 거동을 파악하고자 하였다. 1994년 낙동강에서의 수질오염 사고 이후 4대강 유역에서 설치.운영되고 있는 수질자동 측정망의 TOC 자료를 일자료로 환산하여 사용하였으며, 시계열 자료에 비선형 동역학적(카오스적) 특성이 존재하는지를 알아보기에 앞서 자료의 전처리 과정으로써 3가지의 잡음제거 방법을 적용하였다. 잡음이 제거된 시계열 자료에 비선형 동역학적 거동의 파악을 위해 보편적으로 사용되고 있는 상관차원분석을 실시하였다. 또한 상관차원분석 결과 비선형 동역학적 거동을 나타내는 것으로 판별된 자료에 대하여 그 양상을 가시적으로 알아보기 위해 지체시간$(\tau)$을 적용하여 3차원 위상공간에 도시하였다. 본 연구의 결과, 나주지점에서 측정되고 있는 총유기탄소에 대해 비선형 잡음제거 방법을 적용한 자료가 비선형 동역학적 거동을 내재하고 있는 것으로 나타났으나, 이를 위상공간에 재건하였을 경우 이상한 끌개(strange attractor)의 뚜렷한 구조가 보이지 않았다. 그러나 상관차원분석 결과 잡음이 제거된 자료가 카오스적 특성을 보이므로, 자료의 단기예측을 위한 방법에 기초적인 정보를 제공할 수 있을 것으로 기대된다.

• 전자공학회논문지 IE
• /
• v.49 no.1
• /
• pp.24-29
• /
• 2012

In this paper, we propose a progressive filter for impulse noise reduction. The proposed method uses non-linear filter and linear filter progressively. Non-linear filter reduces abrupt noise pattern. Also, linear filter adjusts filtering direction according to an edge in the image which is filtered by non-linear filter. Thus, our proposed method not only preserves edge, but also reduces noise in uniform region. Experimental results show that our proposed method has better quality than those by existing non-linear and linear progressive filtering methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.12 no.1
• /
• pp.151-156
• /
• 2012

In this paper robust nonlinear image denoising algorithms are introduced for the distribution which is Gaussian in the center and Laplacian in the tails. The distribution is known as the least favorable ${\epsilon}$-contaminated normal distribution that maximizes the asymptotic variance. The proposed filter proves to be the maximum likelihood estimator under the heavy-tailed Gaussian noise environments. It is optimal in the respect of maximizing the efficacy under the above noise environment. Another filter for reducing impulsive noise is proposed by mixing with the myriad filter to propose an amplitude-limited myriad filter. Extensive experiment is conducted with images corrupted with ${\alpha}$-stable noise to analyze the behavior and performance of the proposed filters.

• Journal of the Institute of Convergence Signal Processing
• /
• v.3 no.2
• /
• pp.30-36
• /
• 2002

This paper describes a nonlinear adaptive noise canceler(ANC) using neural networks(NN) based on filter to make up for the drawback of the conventional ANC with the linear adaptive filter. The proposed ANC was tested its noise rejection performance using broadband time-varying noise signal and compared with the ANC of TDL linear filter. Experimental results show that in cases of nonlinear correlations between the noise of primary input and reference input, the neural network based ANC outperforms the linear ANC with respect to mean square error It is also verified that the recurrent NN adaptive filter is superior to the feedforward NN filter. Thus, we identify that the NN adaptive filter is more effective than the linear adaptive filter for rejection of broadband time-varying noise in the ANC.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2001.11b
• /
• pp.135-138
• /
• 2001

위성에 탑재된 전자광학 카메라(Electro-Optical Camera)로부터 획득된 영상에서는 카메라의 스캔 방향과 통일한 방향으로 "줄무늬잡음"이 발생하게 된다. 이는 센서의 특성이 통일하지 않고, 우주라는 열악한 환경에서 영상의 획득이 일어나기 때문이다. 똔 논문에서는 줄무늬잡음을 제거하기 위해 비선형 보정방법을 제안한다. 영상의 준균일성(quasi-homogeneous)과 센서특성의 시불변성(time-invariancy) 가정을 바탕으로, 보정하려는 열의 이웃 열을 참조하여 줄무늬잡음에 의한 오차를 추정하고 이를 최소화한다. 줄무늬 잡음 정도를 추정하기 위해 줄무늬잡음을 바이어스에 의한 것과 특성곡선의 경향 차이에 의한 것으로 나눈다. 바이어스에 의한 오차는 센서가 스캔하는 방향과 통일한 방향으로 통계적 특성을 이용하여 추정한다. 특성곡선의 경향차에 의한 오차는 보정하려는 열에서 동일한 박기 레벨을 갖는 화소들을 조사하고, 이들과 이웃하는 열의 동일 위치에 있는 화소의 밝기 레벨의 통계적 특성을 파악하여 추정한다. 이렇게 추정된 오차를 최소화함으로써, 줄무늬잡음을 효과적으로 제거하였다.적으로 제거하였다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.1B
• /
• pp.37-45
• /
• 2011

We studied noise influence on nonlinear chaotic system by using Logistic data series which is known as a typical nonlinear chaotic system. We regenerated Logistic data series by the method of adding noise according to noise level. And, we performed some analyses such as phase space reconstruction, correlation dimension, BDS statistics, and DVS Algorithms which are known as the methods of nonlinear deterministic or chaotic analysis. If we see the results of analysis, the characteristics of data series are gradually changed from nonlinear chaotic data series to random stochastic data series according to increasing noise level. We applied Low Pass Filter (LPF) and Kalman Filter techniques for the investigation of removing effect of the added noise to data series. Typical nonparametric method cannot distinguish nonlinear random series but the BDS statistic can distinguish the nonlinear randomness of the time series. Therefore this study used the BDS statistic which is well known as nonlinear statistical method for the investigation of randomness of time series for the effect of removing noise of data series. We found that Kalman filter is better method to remove the noise of chaotic data series even for high noise level.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.112-114
• /
• 2014

Generally, images are corrupted by the impulse or AWGN and there are cases where both of these noises are added at once. When it comes to eliminating the noises added to the image, the previous median filter is effective in removing the impulse noise and the average filter is effective for removing AWGN. However, when the complex noises are added, it lacks the noise suppression characteristics, thus in this paper, a non-linear filter algorithm for removing the complex noises was proposed. The simulation results shows the proposed algorithm has excellent de-noising capabilities of compare existing methods.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.6
• /
• pp.83-88
• /
• 2005

In this paper, detection based - adaptive windowed nonlinear filters(DB-AWNF) are proposed for removing one-side impulse noise in infrared image. They are composed of impulse detector and window-size-variable median filters. Impulse detector checks whether current pixel is impulse or not using range function and nonlinear location estimator. If impulse is detected, current pixel is filtered according to four kinds of local masks by use of median filter. If not. current pixel is delivered to output like identity filter. In qualitative view, the proposed could have removed heavy corrupted noise up to $20\%$ and reserved the details of image. In quantitative view, PSNR was measured. The proposed could have 13 - 31[dB] more improved performance than those of median($3{\times}3$) filter and 18 - 25[dB] more improved performance than those of median($5{\times}5$) filter.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.4
• /
• pp.973-979
• /
• 2011

In the transmitting process of image signal processing system, there are several different causes of degradation that have been occurring. The main cause of degradation is attributed to the noise. The most representive method of removing noise of image, which is caused by impulse noise environment, is using the SM(standard median filter). At edge, the filter has a special feature which has a tendency to decrease. As a result, we proposed a nonlinear filter that restores the image considering edge quality in the impulse noise environment. And through the simulation, we compared with the many of the conventional algorithms and the value of the PSNR(peak signal to nise ratio) is better than them and preserve the edge very well. So the nonlinear filter that proposed in this paper is excepted to help improve restoring the images that in impulse noise environment.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2001.11b
• /
• pp.129-133
• /
• 2001

영상에 포함된 잡음은 화질 및 영상의 압축효율을 저하시킨다. 최근 들어, 영상의 에지 성분을 효율적으로 고려하면서 잡음을 제거하기 위하여 다양한 비정적(nonstationary) 영상 모델에 근거한 잡음제거 알고리즘이 제안되어 왔다. 하지만, 기존의 비정적 영상모델에서는 연산량의 부담을 덜기 위하여 각 화소들 사이에 상관관계(correlation)가 없다는 가정을 하고 있어 영상의 미세한 정보들이 필터링에 의하여 훼손된다. 본 논문에서는 영상의 비정적 상관관계를 고려한 적응적 잡음제거 알고리즘을 제시한다. 영상신호는 비정적 평균을 가진다고 가정되며, 또한 각기 다른 정적(stationary) 상관관계를 가지는 부분 영상으로 분리된다고 가정된다. 제안된 영상 모델에서의 공분산(co-variance) 행렬의 특수한 구조를 이용하여 계산적으로 효율적인 FFT에 기반한 선형 minimum mean square error 필터를 유도한다. 제안된 영상 모델의 정당성 및 알고리즘의 효율성을 제시한다.