• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.445-454
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• 2006

We describe a solution to the Ornstein-Uhlenbeck equation $\frac{dI}{dt}-\frac{1}{\tau}$I(t)=cV(t) where V(t) is a constant multiple of a Gaussian white noise. Our solution is based on a discrete set of Gaussian white noise obtained by taking sample points from a sum of single frequency harmonics that have random amplitudes, random frequencies, and random phases. Hence, it is different from the solution by the standard random walk using random numbers generated by the Box-Mueller algorithm. We prove that the power of the signal has the additive property, from which we derive that the Lyapunov characteristic exponent for our solution is positive. This compares with the solution by other methods where the noise is kept to be in an error range so that its Lyapunov exponent is negative.

• ETRI Journal
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• v.35 no.4
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• pp.644-654
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• 2013

In this paper, a signal detection scheme for cognitive radio (CR) based on the Bussgang theorem is proposed. The proposed scheme calculates the statistical difference between Gaussian noise and the primary user signal by applying the Bussgang theorem to the received signal. Therefore, the proposed scheme overcomes the noise uncertainty and gives scalable complexity according to the zero-memory nonlinear function for a mobile device. We also present the theoretical analysis on the detection threshold and the detection performance in the additive white Gaussian noise channel. The proposed detection scheme is evaluated by computer simulations based on the IEEE 802.22 standard for the wireless regional area network. Our results show that the proposed scheme is robust to the noise uncertainty and works well in a very low signal-to-noise ratio.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.1-6
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• 2007

In the process of the acquisition, storage, transmission of signals, noises are generated by various causes and the degradation phenomenon by noises tends to generate serious errors for the signal with information. So, in order to analyze and remove these noises, studies on numerous mathematical methods such as the Fourier transform have been implemented. And recently there have been many ongoing wavelet-based denoising algorithms representing excellent characteristics in time-frequency localization and multiresolution analysis, but the method to remove additive white Gaussian noise (AWGN) and the impulse noise simultaneously was not given. So, to reconstruct the corrupted signal by noises, in this paper a novel wavelet-based denoising algorithm was proposed and using signal-to-noise ratio (SNR) this method was compared to conventional methods.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.869-878
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• 2009

Image restoration techniques such as noise reduction and contrast enhancement have been researched for enhancing a contaminated image by the noise. An image degraded by additive random noise can be enhanced by noise reduction. Sigma filtering is one of the most widely used method to reduce the noise. In this paper, we propose a new sigma filter algorithm based on noise variance estimation which effectively enhances the degraded image by noise. Specifically, the Bartlett test is used to measure the degree of noise with respect to the degree of image feature. Simulation results are also given to show the performance of the proposed algorithm.

• 전자공학회논문지 IE
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• v.49 no.2
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• pp.9-17
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• 2012

One of the problems on the application of the speech recognition system in the real world is the degradation of the performance by acoustical distortions. The most important source of acoustical distortion is the additive noise. This paper describes a spectral compensation technique based on a spectral peak enhancement scheme followed by an efficient noise subtraction scheme for noise robust speech recognition. The proposed methods emphasize the formant structure and compensate the spectral tilt of the speech spectrum while maintaining broad-bandwidth spectral components. The recognition experiments was conducted using noisy speech corrupted by white Gaussian noise, car noise, babble noise or subway noise. The new technique reduced the average error rate slightly under high SNR(Signal to Noise Ratio) environment, and significantly reduced the average error rate by 1/2 under low SNR(10 dB) environment when compared with the case of without spectral compensations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.173-175
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• 2017

With the rapid progress of the digital area has come the increase in demand for multi-media services. Imaging processing as a result is being hailed as a technological field that can offer smart and efficient methods for the processing and analysis of images. In general, noise exist in various types, depending on the cause and form. Some leading examples of noise are AWGN(additive white Gaussian noise), salt and pepper noise and complex noise. This study suggests an algorithm to remove complex noise by using the standard deviation and noise density of the partial mask in order to effectively remove complex noise in images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2798-2804
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• 2010

The audio and image signal are corrupted by various noises in signal processing, many studies are being accomplished to restore those signals. In this paper, the algorithm is proposed to remove additive Gaussian noise and impulse noise at one dimension signal like an speech signal. The algorithm is composed to remove Gaussian noise after removing impulse noise. And the method using wavelet coefficient accumulation is used to remove the Gaussian noise, and the median filter based on element deviation is applied to remove the impulse noise. Also we compare existing methods using SNR(signal-to-noise ratio) as the standard of judgement of improvemental effect.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.183-190
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• 2013

In this paper, we propose a spatially adaptive noise detection and removal algorithm for a single degraded image. Under the assumption that an observed image is Gaussian-distributed, the noise information is estimated by local statistics of degraded image, and the degree of the additive noise is detected by the local statistics of the estimated noise. In addition, we describe a noise removal method taking a modified Gaussian filter which is adaptively determined by filter parameters and window size. The experimental results demonstrate the capability of the proposed algorithm.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.115-118
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• 1998

본 연구에서는 입력 음성 특징 파라메터를 선형 및 비선형 변환함으로써 음성 특징의 1 차, 2 차 및 고차 통계치를 정규화하였다. 이러한 정규화 기법을 통해서 부가잡음 환경에서의 음성인식 성능향상을 얻을 수 있었다.