• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.127-129
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• 2012

Due to the prevalence of digital camera, multi-media etc. the image is being used in everyday life. However, noise always damages the image and the image denoising technology is important part for improving the image visual quality. There are many existing methods to remove noise such as wiener filter, mean filter and VisuShrink etc. However, they perform not good enough for denoising. Hence, in this paper we proposed a hybrid filter algorithm which consists of wiener filter and modified wavelet based thresholding method using adaptive threshold and thresholding function. The proposed algorithm shows not only better low frequency and high frequency property, but also the outstanding noise suppression and edge preservation properties.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1793-1801
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• 2016

Speech enhancement is a challenging problem due to the diversity of noise sources and their effects in different applications. The goal of speech enhancement is to improve the quality and intelligibility of speech by reducing noise. Many research works in speech enhancement have been accomplished in English and other European Languages. There has been limited or no such works or efforts in the past in the context of Tamil speech enhancement in the literature. The aim of the proposed method is to reduce the background noise present in the Tamil speech signal by using wavelets. New modified thresholding function is introduced. The proposed method is evaluated on several speakers and under various noise conditions including White Gaussian noise, Babble noise and Car noise. The Signal to Noise Ratio (SNR), Mean Square Error (MSE) and Mean Opinion Score (MOS) results show that the proposed thresholding function improves the speech enhancement compared to the conventional hard and soft thresholding methods.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.8
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• pp.1309-1314
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• 2008

Seismic data is often contaminated with high-energy, spatially aliased noise, which has proven impractical to attenuate using Fourier techniques. Wavelet filtering, however, has proven capable of attacking several types of localized noise simultaneously regardless of their frequencies. In this study a 2-D stationary wavelet transform is used to decompose seismic data into its wavelet components. A threshold is applied to these coefficients to attenuate high amplitude noise, followed by an inverse transform to reconstruct the seismic trace. The stationary wavelet transform minimizes the phase-shift errors induced by thresholding that occur when the conventional discrete wavelet transform is used.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.261-265
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• 2011

An adaptive wavelet transformation method with high order accuracy is proposed to allow efficient and accurate flow computations. While maintaining the original numerical accuracy of a conventional solver, the scheme offers efficient numerical procedure by using only adapted dataset. The main algorithm includes 3rd order wavelet decomposition and thresholding procedure. After the wavelet transformation, 3rd order of spatial and temporal accurate high order interpolation schemes are executed only at the points of the adapted dataset. For the other points, high order of interpolation method is utilized for residual evaluation. This high order interpolation scheme with high order adaptive wavelet transformation was applied to unsteady Euler flow computations. Through these processes, both computational efficiency and numerical accuracy are validated even in case of high order accurate unsteady flow computations.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.467-476
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• 2004

Recently, an approximation of a wavelet series has been developed in the analyses of an unknown function. Most of articles have been studied on thresholding and shrinkage methods for its wavelet coefficients based on (non)parametric and Bayesian methods when the sample size is considered as a maximum resolution in wavelet series. In this paper, regardless of the sample size, we are focusing only on the choice of a maximum resolution in wavelet series. We propose a Bayesian approach to the choice of a maximum resolution based on the linear combination of the wavelet basis functions.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.5
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• pp.330-339
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• 2015

Power quality (PQ) problems are becoming a big issue, since delicate complex electronic devices are widely used. We present a new denoising technique using discrete wavelet transform (DWT), where a modified correlation thresholding is used in order to reliably detect the PQ disturbances. We consider various PQ disturbances on the basis of IEEE-1159 standard over noisy environments, including voltage swell, voltage sag, transient, harmonics, interrupt, and their combinations. These event signals are decomposed using DWT for the detection of disturbances. We then evaluate the PQ disturbance detection ratio of the proposed denoising scheme over Gaussian noise channels. Simulation results also show that the proposed scheme has an improved signal-to-noise ratio (SNR) over existing scheme.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.465-466
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• 2015

배터리 관리시스템(BMS;battery management system)의 중요 고려요소인 SOC(state-of-charge) 및 SOH(state-of-health)의 전기적 등가회로 모델 기반 고성능 추정의 전제 조건은 배터리 단자전압의 안정된 실험데이터 확보이다. 그러나, 예상치 않은 에러로 인해 배터리 단자전압에 노이즈 성분이 포함될 경우 SOC 및 SOH 추정알고리즘의 성능저하가 우려된다. 이를 위해, 본 논문은 이산 웨이블릿 변환(DWT;discrete wavelet transform)의 다해상도 분석(MRA;multi resolution analysis) 레벨에 따른 디노이징 최적 성능을 소개하고자 한다. 하드 임계화(hard-thresholding) 및 소프트 임계화(soft-thresholding) 기법에 따른 디노이징 성능 차이를 보이고, 각 임계화 기법 적용 시 디노이징 최적 성능을 보이는 레벨을 선택한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.12
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• pp.18-25
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• 2005

This paper presents a supercompact multi-wavelet scheme and its application to fluid simulation data. The supercompact wavelet method is an appropriate wavelet for fluid simulation data in the sense that it can provide compact support and avoid unnecessary interaction with remotely located data (e.g. across a shock discontinuity or vortices). thresholding for data compression is applied based on a covariance vector structure of multi-wavelets. The extension of this scheme to three dimensions is analyzed. The numerical tests demonstrate that it can allow various analytic advantages as well as large data compression ratios in actual practice.

• Nuclear Engineering and Technology
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• v.39 no.3
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• pp.221-230
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• 2007

Wavelet theory was applied to detect a singularity in a reactor power signal. Compared to Fourier transform, wavelet transform has localization properties in space and frequency. Therefore, using wavelet transform after de-noising, singular points can easily be found. To test this theory, reactor power signals were generated using the HANARO(a Korean multi-purpose research reactor) dynamics model consisting of 39 nonlinear differential equations contaminated with Gaussian noise. Wavelet transform decomposition and de-noising procedures were applied to these signals. It was possible to detect singular events such as a sudden reactivity change and abrupt intrinsic property changes. Thus, this method could be profitably utilized in a real-time system for automatic event recognition(e.g., reactor condition monitoring).

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.595-607
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• 2010

This paper addresses the problem of reducing the speckle noise in SAR images by wavelet transformation, using a non-local means(NLM) filter originated for Gaussian noise removal. Log-transformed SAR image makes multiplicative speckle noise additive. Thus, non-local means filtering and wavelet thresholding are used to reduce the additive noise, followed by an exponential transformation. NLM filter is an image denoising method that replaces each pixel by a weighted average of all the similarly pixels in the image. But the NLM filter takes an acceptable amount of time to perform the process for all possible pairs of pixels. This paper, also proposes an alternative strategy that uses the t-test more efficiently to eliminate pixel pairs that are dissimilar. Extensive simulations showed that the proposed filter outperforms many existing filters terms of quantitative measures such as PSNR and DSSIM as well as qualitative judgments of image quality and the computational time required to restore images.