• Journal of Advanced Marine Engineering and Technology
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• 제32권8호
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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.

• 전자공학회논문지S
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• 제34S권10호
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• pp.112-119
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• 1997

Zerotreeimage coding is known as a simple and effective image comprssion algorithm. It has the property that the compression is generated in order of improtance. Conventionally, a fixed threshold is applied to the entire wavelet coefficients regardless of frequency and local features of an image. In this paper, we propose a new zerotree coding scheme with adaptive threshold. The adaptive threshold is determined by human visual characteristics. It is shown that the image quality of the proposed method is better than that of the conventional method.

• Journal of Information Processing Systems
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• 제12권1호
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• pp.46-56
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• 2016

Dynamic textures are videos that exhibit a stationary property with respect to time (i.e., they have patterns that repeat themselves over a large number of frames). These patterns can easily be tracked by a linear dynamic system. In this paper, a model that identifies the underlying linear dynamic system using wavelet coefficients, rather than a raw sequence, is proposed. Content based threshold filtering based on Set Partitioning in a Hierarchical Tree (SPIHT) helps to get another representation of the same frames that only have low frequency components. The main idea of this paper is to apply SPIHT based threshold filtering on different bands of wavelet transform so as to have more significant information in fewer parameters for singular value decomposition (SVD). In this case, more flexibility is given for the component selection, as SVD is independently applied to the different bands of frames of a dynamic texture. To minimize the time complexity, the proposed model is implemented on a graphics processing unit (GPU). Test results show that the proposed dynamic system, along with a discrete wavelet and SPIHT, achieve a highly compact model with better visual quality, than the available LDS, Fourier descriptor model, and higher-order SVD (HOSVD).

• 한국지능시스템학회논문지
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• 제14권4호
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• pp.473-480
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• 2004

본 논문에서는 컬러 영상에서의 새로운 에지 검출 알고리즘을 제안한다. 제안된 적응 형태학적 WCNN알고리즘은 적응 형태학과 WCNN알고리즘으로 구성된다. 이는 입력된 컬러 영상의 임계값에 따라 적응 형태학을 이용하여 경계면의 차를 레벨업 시킨 후 WCNN 알고리즘을 이용하여 최적의 에지를 검출한다. 또한, 기존의 고정 마스크에지 검출방식을 탈피하여, 영상의 임계값의 차에 따라 가변적으로 변화하는 가변 BBM(Beak Y. H, Byun O. H, Moon S. R)마스크를 사용한다. 제안된 알고리즘의 기존의 연구에 비해 유용성을 검증하기 위해 본 논문은 30개의 컬러 영상의 모의 실험을 제공한다.

• Journal of Information Processing Systems
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• 제14권4호
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• pp.941-947
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• 2018

Since the amplitudes of spin echo train in nuclear magnetic resonance logging (NMRL) are small and the signal to noise ratio (SNR) is also very low, this paper puts forward an improved de-noising algorithm based on wavelet transformation. The steps of this improved algorithm are designed and realized based on the characteristics of spin echo train in NMRL. To test this improved de-noising algorithm, a 32 points forward model of big porosity is build, the signal of spin echo sequence with adjustable SNR are generated by this forward model in an experiment, then the median filtering, wavelet hard threshold de-noising, wavelet soft threshold de-noising and the improved de-noising algorithm are compared to de-noising these signals, the filtering effects of these four algorithms are analyzed while the SNR and the root mean square error (RMSE) are also calculated out. The results of this experiment show that the improved de-noising algorithm can improve SNR from 10 to 27.57, which is very useful to enhance signal and de-nosing noise for spin echo train in NMRL.

• Journal of the Korean Data and Information Science Society
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• 제20권6호
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• pp.1191-1197
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• 2009

Most Bayesian approaches to model selection of wavelet analysis have drawbacks that computational cost is expensive to obtain accuracy for the fitted unknown function. To overcome the drawback, this article introduces loss functions which are criteria for level dependent threshold selection in wavelet based Bayesian methods with arbitrary size and regular design points. We demonstrate the utility of these criteria by four test functions and real data.

• Journal of Information Processing Systems
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• 제1권1호
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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.

• 한국자기공명학회논문지
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• 제5권2호
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• pp.66-72
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• 2001

The noise suppression of time domain NMR data by discrete wavelet transform with high order Daubechies wavelet coefficients exhibits severe peak distortion and incomplete noise suppression near real signal. However, the fact that even a shift averaged Harr wavelet transform with a set of Daubechies wavelet coefficients (1/2, -l/2) can be used as a new and excellent tool to distinguish real peaks from the noise contaminated NMR signal is introduced. New algorithms of shift averaged Harr wavelet were developed and quantitatively evaluated in terms of threshold and signal to noise ratio (SNR).

• 대한의용생체공학회:의공학회지
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• 제17권4호
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• pp.507-514
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• 1996

The automated ECG diagnostic systems in hospital have a low P-wave detection capacity in case of some diseases like conduction block. The purpose of this study is to improve the P-wave detection ca- pacity using wavelet transform. The first procedure is to remove baseline drift by subtracting the median filtered signal from the original signal. The second procedure is to cancel ECG's QRS-T complex from median filtered signal to get P-wave candidate. Before we subtracted the templete from QRS-T complex, we estimated the best matching between templete and QRS-T complex to minimize the error. Then, wavelet transform was applied to confirm P-wave. In particular, haiti wavelet was used to magnify P-wave that consisted of low frequency components and to reject high frequency noise of QRS-T complex cancelled signal. Finally, p-wave was discriminated and confirmed by threshold value. By using this method, We can got the around 95.1% P-wave detection. It was compared with contextual information.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 춘계종합학술대회
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• pp.524-527
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• 2005

최근, 사회는 고도의 디지털 정보화 시대로 급속히 발전하고 있다. 그러나 여전히 신호를 처리하는 과정에서 여러 가지 원인에 의해 잡음이 발생하고 있으며, 이러한 잡음들을 제거하기 위한 다양한 방법들이 연구되고 있다. 잡음을 제거하기 위해 기존에 FFT와 STFT 등이 있었으나, 신호에 대한 시간정보를 알 수 없고 시간-주파수 국부성이 상충관계를 갖는다. 따라서 이러한 한계를 극복하기 위해, 다중해상도 해석이 가능한 웨이브렛기반의 잡음 제거 기법들이 신호처리 분야에서 응용되고 있다. 그러나 threshold와 상관관계를 이용한 잡음 제거 방법은 잡음의 통계적 특징만을 반영함에 따라, 많은 잡음들이 edge로써 판단될 수 있으며, AWGN과 임펄스 잡음을 동시에 제거하기 위한 방법을 제공하지 않는다. 따라서 본 논문에서는 웨이브렛기반의 새로운 잡음 제거 방법을 제시하여, 기존의 방법들과 비교하였다.