• 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.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.32-35
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• 2005

Image denoising is basic work for image processing, analysis and computer vision. This paper proposes a novel algorithm based on wavelet threshold for image denoising, which is combined with the linear CLS (Constrained Least Squares) filtering and thresholding methods in the transform domain. We demonstrated through simulations with images contaminated by white Gaussian noise that our scheme exhibits better performance in both PSNR (Peak Signal-to-Noise Ratio) and visual effect.

• Bulletin of the Korean Chemical Society
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• v.14 no.1
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• pp.95-100
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• 1993

A noise-induced transition is presented for a bistable system subjected to a multiplicative random force, which is singular at the unstable state. The stationary probability distribution is obtained from the Fokker-Planck equation and the effects of the singularity is analyzed. On the basis of noise-induced phase transition with Gaussian white noise, the relaxation time and the transition rate of the system are evaluated up to the first order correction of D. In the parameter region v < l, the transition rates decrease as the exponent v goes to 1 and as the coefficient of the linear term of the kinetic equation increases.

• Bulletin of the Korean Chemical Society
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• v.16 no.11
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• pp.1113-1118
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• 1995

The Schlo'gl model with the first order transition for a photochemical reaction is considered to study the dynamic behaviors in the neighborhood of the Gaussian white noise by obtaining the explicit results of the time-dependent variance and time correlation function with the aid of approximate methods based on the stationary properties of the system. Then, we discuss the effect of external noise strength on the stability of the model at steady states in detail.

• Bulletin of the Korean Chemical Society
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• v.16 no.11
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• pp.1119-1121
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• 1995

The method for the Schlo"gl model with the first order transition is extended to the Scho;gl model with the second order transition for a photochemical reaction. We obtain the explicit results of the time-dependent average and the time correlation function at the unstable steady state of the model in the neighborhood of the Gaussian white noise and then discuss the effect of noise on the dynamic properties.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.5
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• pp.726-731
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• 1986

An effective filtering algorithm which can reduce noise and preserve edges for the restoration of an image degraded by additive white Gaussian noise is presented. The algorithm proposed in this paper is an extension of Lee's algorithm modified to use local gradient information as well as local statistics. It does not require image modeling, and removes noise along the orientaiton of edges so that it does not blur the edge.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2E
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• pp.47-54
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• 1997

This paper presents two results on intersymbol interferences in baseband digital communication over an additive white Gaussian noise channel-the interferences due to mismatched square-root raised-cosine filters, in which the filters have different roll-off factors, and / or due to sampling-time jitter. The result for the mismatched filters is that even the jitter-free sampling causes intersymbol interference and it is negligibly small for a wide range of signal-to-noise ratio up to 10dB, for the roll-off factor ranging from 0.2 to 0.5, the mismatch loss being within 0.1dB from the optimum at around 10-6 .For jitter interference an approximation formula for the bit error probability is derived in case of the matched filters, which shows how the roll-off factors and the amount of jitter affect the system performance. The formula is reasonably accurate.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.26-29
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• 2001

In a target tracking problem the radar glint noise has non-Gaussian heavy-tailed distribution and will seriously affect the target tracking performance. In most nonlinear situations an Extended Robust Kalman Filter(ERKF) can yield acceptable performance as long as the noises are white Gaussian. However, an Extended Robust $H_{\infty}$ Filter (ERHF) can yield acceptable performance when the noises are Laplacian. In this paper, we use the Interacting Multiple Model(IMM) estimator for the problem of target tracking with glint noise. In the IMM method, two filters(ERKF and ERHF) are used in parallel to estimate the state. Computer simulations of a real target tracking shows that hybrid filter used the IMM algorithm has superior performance than a single type filter.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.655-658
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• 1999

In this study, We derived Recursive Least Squares(RLS) algorithm with adaptive maximum -likelihood channel estimate for digital pulse amplitude modulated sequence in the presence of intersymbol interference and additive white Gaussian noise. RLS algorithms have better convergence characteristics than conventional algorithms, LMS Least Mean Squares) algorithms.