• Journal of IKEEE
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• v.22 no.4
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• pp.1242-1245
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• 2018

In this paper, we have implemented a network that overcomes the over-blurring phenomenon that occurs when removing Gaussian noise. In the conventional filtering method, blurring of the original image is performed to remove noise, thereby eliminating high frequency components such as edges and corners. We propose a network that reducing over blurring while maintaining denoising performance by adding denoised high frequency components to denoisers based on CNN.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.73-76
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• 2018

A Gaussian noise is caused by surrounding environment or channel interference when transmitting image. The noise reduces not only image quality degradation but also high-level image processing performance. The Non-Local Means (NLM) filter finds similarity in the neighboring sets of pixels to remove noise and assigns weights according to similarity. The weighted average is calculated based on the weight. The NLM filter method shows low noise cancellation performance and high complexity in the process of finding the similarity using weight allocation and neighbor set. In order to solve these problems, we propose an algorithm that shows an excellent noise reduction performance by using Summed Square Image (SSI) to reduce the complexity and applying the weighting function based on a cosine Gaussian kernel function. Experimental results demonstrate the effectiveness of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.112-117
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• 1999

In case of spectral subtraction for noise robust speech recognition system, this method often makes loss of speech signal. In this study, we propose a method that variation and determination of Gaussian function at semi-continuous HMM(Hidden Markov Model) is made on the basis of SNR criteria function, in which SNR means signal to noise ratio between estimation noise and subtracted signal per frame. For proving effectiveness of this method, we show the estimation error to be related with the magnitude of estimated noise through signal waveform. For this reason, Gaussian function is varied and determined by SNR. When we test recognition rate by computer simulation under the noise environment of driving car over the speed of 80㎞/h, the proposed Gaussian decision method by SNR turns out to get more improved recognition rate compared with the frequency subtracted and non-subtracted cases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.10
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• pp.797-810
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• 1990

The error rate equations of digitally modulated signals transmitted through the Gaussian noise and tone multiple interference channel have been derived. Using the derived equations of error probabilities in the environments of Gaussian noise tone interferer and Gaussian noise multiple interferer, the error rate performances of various digitally modulated signals have been evaluated, and compared in graphs as a function of average carrier to tone interferer power ratio(CIR), average carrier to multiple interferer power ratio(CIT) and the average carrer-to-Gaussian noise powr ratio(CIR). In this paper, the modulation schemes such as amplitude shift keying (ASK), phase shift keying(PSK), frequency shift keying(FSK), minimum shift keying(MSK), quadrature amplitud modulation(QAM) and amplitude phase shift keying(APK) have been selected for the study of performance comparison. The results of comparison show us that, in low bits/sec/Hz, PSK is superior to the other schemes, but in high bits/sec/Hz, mixed multi ary type is better than single multi ary type. And in strong noise evironment, the multiple interferer has much influence than tone interferer, however, in low noise environment. the mojor error factor is tone interferer. But tone interference effect nearly disappears over specified CIR level about 20[dB]. And the modulation schemes using amplitude are heavily influenced by multiple interference.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.205-208
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• 2007

Due to the development and wide use of digital multimedia broadcasting (DMB) and Wireless Broadband Internet (WiBro), the digital contents industry using images has been progressed. Therefore, the image processing has been applied in a variety of fields and in order to transmit and conserve accurate information, the degradation phenomenon for images should be removed. As a representative cause of the degradation phenonenon, noise has become known and Gaussian noise occurs in the process of transmission. Diverse researches for Gaussian noise removal have been implemented and a great number of algorithms have been proposed until now. In this paper, for mage restoration an algorithm using the adaptive threshold value is proposed in Gaussian noise environment and the threshold value is established by using the histogram of edge image. And from simulation results, the noise removal performance of the proposed method is proven using mean square error (MSE) and peak signal to noise ratio (PSNR).

• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.93-99
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• 2024

The digital medical imaging, especially, computed tomography (CT), should necessarily be considered in terms of noise distribution caused by converting to X-ray photon to digital imaging signal. Recently, the denoising technique based on deep learning architecture is increasingly used in the medical imaging field. Here, we evaluated noise reduction effect according to various noise types based on the U-net deep learning model in the lung CT images. The input data for deep learning was generated by applying Gaussian noise, Poisson noise, salt and pepper noise and speckle noise from the ground truth (GT) image. In particular, two types of Gaussian noise input data were applied with standard deviation values of 30 and 50. There are applied hyper-parameters, which were Adam as optimizer function, 100 as epochs, and 0.0001 as learning rate, respectively. To analyze the quantitative values, the mean square error (MSE), the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. According to the results, it was confirmed that the U-net model was effective for noise reduction all of the set conditions in this study. Especially, it showed the best performance in Gaussian noise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.6C
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• pp.629-636
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• 2006

In array signal processing, high order statistics can be used to estimate parameters from signal of sums of complex exponential. In this paper, we derive two types of direction finding algorithms which use the fourth-order cumulant and moment of the received array data. Since the fourth order cumulant can suppress the Gaussian noise, the response of MPM has better noise immunity than the conventional approaches. The performance of each method in regard to the probability of resolution and SNR in the presence of the Gaussian noise is investigated. As a result, the proposed method applied to the fourth-order statistic can find DOA more correctly in the presence of the Gaussian noise.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.85-95
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• 1995

The error intensity models for the ML estimation of a signal parameter have been developed in a companion paper [1]. While the methods described in [1] are applicable to any estimation problem with continuous parameters, our main application in this paper is the time delay estimation, and comparisons among the models derived in [1] (i.e. LC, LM, and ALM models)have been made. We first consider the case where only additive Gaussian noise is involved, and then the shot noise environment where coherent impulsive noise is also involved in addition to the Gaussian noise. We compare the models in terms of the probability of error, MSE(Mean Squared Error), and the computational complexity, which are the most important performance criteria in the analysis of parameter estimation. In conclusion, the ALM model turned out to be the most adequate model of all from the viewpoints of the criteria mentioned above.

• 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.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.9 no.1
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• pp.9-16
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• 2005

The random signals defined as sums of the single frequency sinusoidal signals with random amplitudes and random phases or equivalently sums of functions obtained by adding a Sine and a Cosine function with random amplitudes, are used in the double randomization method for the Monte Carlo solution of the turbulent systems. We show that these random signals can be used for studying the properties of the Johnson noise by proving that constant multiples of these signals with uniformly distributed frequencies in a fixed frequency band satisfy the properties of the Gaussian white noise.