• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.591-596
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• 2011

Image denoising is a key issue in all image processing researches. Generally, the quality of an image could be corrupted by a lot of noise due to the undesired conditions of image acquisition phase or during the transmission. Many approaches to image restoration are aimed at removing either Gaussian or impulse noise. Nevertheless, it is possible to find them operating on the same image, which is called mixed noise and it produces a hard damage. In this paper, we proposed noise type classification method and a mixed nonlinear filter for mixed noise suppression. The proposed filtering scheme applies a modified adaptive switching median filter to impulse noise suppression and an efficient nonlinear filer was carried out to remove Gaussian noise. The simulation results based on Matlab show that the proposed method can remove mixed Gaussian and impulse noise efficiently and it can preserve the integrity of edge and keep the detailed information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2265-2271
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• 2014

In modern society digital equipments that are related with various hardware and software are popularized, and digital images are widely applied in the field of production and scientific research. In general, however, images are degraded by the noise in the process of transmission and storage. In this paper, to reduce the influence of mixed noises, the algorithm in which noises in the space area are classified into impulse noise and Gaussian noise and this is processed by applying weighted value, while that is processed by modified nonlinear filter is proposed. And the excellence of the proposed algorithm is judged by PSNR(peak signal to noise ratio).

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.557-562
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• 2004

This paper deals with a robust mixed ${H_2}/{H_{\infty}}$ filter design problem for a nonlinear dynamic system modeled as a T-S fuzzy system. Integral quadratic constraints are used to describe various kinds of uncertainties of the plant. A sufficient condition for solvability is given in terms of linear matrix inequality problem which can be efficiently solved using a convex optimization technique. In order to demonstrate the Proposed method, a numerical design example is provided.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.629-635
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• 2017

In this paper, we proposed a nonlinear synthesis filter for noise reduction to reduce the effects of Gaussian noise and impulse noise. When the centralization of the local mask is judged to be Gaussian noise by the noise judgment, the weight value of the weight filter are applied differently according to the spatial weight filter and the pixel change by using the sample variance in the local mask. And if it is determined as the impulse noise, we proposed an algorithm that applies different weights of local histogram weight filter and standard median filter according to noise density of mask. In order to evaluate the performance of the proposed filter algorithm, we used PSNR(peak signal to noise ratio) and compared existing methods and proposed filter algorithm in the mixed noise environment with Gaussian noise, impulsive noise, and two noises mixed.

• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.336-340
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• 2011

Many approaches to image restoration are aimed at removing either gauss or impulse noise. This is because both types of degradation processes are distinct in nature, and hence they are easier to manage when considered separately. Nevertheless, it is possible to find them operating on the same image, which produces a hard damage. This happens when an image, already contaminated by Gaussian noise in the image acquisition procedure, undergoes impulsive corruption during its digital transmission. Here we proposed an algorithm first judge the type of the noise according to the difference values of pixel's neighborhood region and impulse noise's characteristic. Then removes the gauss noise by modified weighted mean filter and removes the impulse noise by modified nonlinear filter. The result of computer simulation on test images indicates that the proposed method is superior to traditional filtering algorithms. The proposed method can not only remove mixed noise effectively, but also preserve image details.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.5
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• pp.848-852
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• 2001

The biomedical signals such as ECG, EMG, EEG, and etc are very Important information to diagnose patients The signal is hard to filter the noise because that is mixed with a lot of noise and biomedical signal has the properties of nonlinear and time-variance. So, we will filter under the measure environment for system or patient. But the general adaptive fillet has brought on the distortion of signal because the adaptive filter adjust the filter coefficient with the fixed order of filter, that filter has the unsuitable order in each other environment. So we propose the dynamic structure adaptive filter that is used for improving that disadvantage. In experiment, we obtain the optimal order of adaptive filter and have food results.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.14-16
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• 2007

The statistics for the Gaussian noise mixed with impulsive noise are modelled. The denoising algorithm called amplitude-limited sample average filter is derived, which is optimal in terms of minimizing mean square errors under the assumption that contaminating noise is heavy-tailed Gaussian distributed. Its performance is shown to be excellent when image is corrupted mainly with Gaussian noise. However, it shows visually grainy output as the amount of impulsive noise increases. In order to overcome such problems, it is combined with the myriad filter to propose an amplitude-limited myriad filter. Simulation shows it effectively removes both Gaussian and impulsive noise, not blurring edges severey.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2447-2453
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• 2013

The digital images are being degraded by noise in the process of acquisition, storage and transmission, Gaussian or impulse noise is the representative noise. Meanwhile, the image has lots of tendency to be degraded by complex noise, so various researches are being conducted for reducing these complex noise. In this paper, to remove complex noise, the algorithm processed by modified switching median filter and modified adaptive weighted filter according to the result after judging the kinds of noise is proposed. In the simulation result, excellent denoising capabilities. Furthermore, we compared proposed algorithm with existing methods for objective judgement, and PSNR(peak signal to noise ratio) is used by the criterion of judgement.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.11
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• pp.1523-1534
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• 2000

The performance of a number of existing dynamic subgrid-scale(SGS) models is evaluated in large-eddy simulations(LES) of two prototype transitional and turbulent shear flows, a planar jet and a channel flow. The dynamic SGS models applied include the dynamic Smagorinsky model(DSM);Germano et al. 1991, Lully 1992), the dynamic tow-component model(DTM; Akhavan et al. 2000), the dynamic mixed model(DMM;Zang et al, 1993). and the dynamic two-parameter model(DTPM; Salvetti & Banerjee 1995). The results are compared with those for DNS for their evaluation. The LES results demonstrate the superior performance of DTM with use of a sharp cutoff filter and DMM with use of a box filter, as compared to their respect counterpart DSM, in predicting the mean statistics, spectra and large-scale structure of the flow, Such features of DTM and DMM derive from the construction of the models in which tow separate terms are included to represent the SGS interactions; a Smagorinsky edd-viscosity term to account for the non-local interactions, and a local-interaction term to account for the nonlinear dynamics between the resolved and subgrid scales in the vicinity of the LES cutoff. As well, overall the SGS models using a sharp cutoff filter are more successful than those using a box filter in capturing the statistics and structure of the flow. Finally, DTPM is found to be compatible or inferior to DMM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2239-2246
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• 2011

In image signal processing, image signal is corrupted by various noises and caused the degradation phenomenon. And Images often corrupted by AWGN(additive white gaussian noise) and impulse noise which called mixed noise. In this paper, the algorithm is proposed to remove mixed noise while keeping edge information. The proposed algorithm first classifies the noise type, if the classify result is AWGN, then the mean of the output after using self-adaptive weighted mean filter and median value will be the outfiltering value. And if the noise type is impulse noise, then the noise is removed by a modified nonlinear filter. Also we compare existing methods through the simulation and using PSNR(peak signal to noise ratio) as the standard of judgement of improvement effect. The result of computer simulation on test images indicates that the proposed method is superior to traditional filtering algorithms.