• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2956-2958
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• 1999

The classical solution to the noise removal problem is the Wiener filter, which utilizes the second-order statistics of the Fourier decomposition. We discuss a Bayesian formalism which gives rise to a type of wavelet threshold estimation in non-parametric regression. A prior distribution is imposed on the wavelet coefficients of the unknown response function, designed to capture the sparseness of wavelet expansion common to most application. For the prior specified, the posterior median yields a thresholding procedure

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.45-55
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• 1999

In this paper, we used the WLMS(Wavelet domain Least Mean Square) adaptive filter based on the wavelet transform to cancel grain noise. Usually, grain noise occurs in changes of the crystalline structure of metals in high temperature environment. It makes the detection of flaw difficult. The WLMS adaptive filtering algorithm establishes the faster convergence rate by orthogonalizaing the input vector of adaptive filter as compared with that of LMS adaptive filtering algorithm in time domain. We implemented the WLMS adaptive filter by using the delayed version of the primary input vector as the reference input vector and then implemented the CA-CFAR(Cell Averaging- Constant False Alarm Rate) threshold estimator. CA-CFAR threshold estimator enables to detect the flaw and back echo signals automatically. Here, we used the output signals of adaptive filter as its input signal. To Cow the statistical characteristic of ultrasonic signals corrupted by grain noise, we performed run test. The results showed that ultrasonic signals are nonstationary signal, that is, signals whose statistical properties vary with time. The performance of each filter is appreciated by the signal-to-noise ratio. After LMS adaptive filtering in time domain, SNR improves to about 2-3㏈ but after WLMS adaptive filtering in wavelet domain, SNR improves to about 4-6㏈.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.11 s.116
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• pp.1149-1157
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• 2006

Donoho et al. suggested a wavelet thresholding denoising method based on discrete wavelet transform. This paper proposes an improved denoising method using a new thresholding function based on translation-invariant wavelet for underwater acoustic measurement. The conventional wavelet thresholding denoising method causes Pseudo-Gibbs phenomena near singularities due to the lack of translation-invariant of the wavelet basis. To suppress Pseudo-Gibbs phenomena, a denoising method combining a new thresholding function based on the translation-invariant wavelet transform is proposed in this paper. The new thresholding function is a modified hard-thresholding to each node according to the discriminated threshold so as to reject unknown external noise and white gaussian noise. The experimental results show that the proposed method can effectively eliminate noise, extract characteristic information of radiated noise signals.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.302-305
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• 2006

In this paper, we have proposed a novel method using quantization of wavelet coefficients for secret data communication. First, speech signal is partitioned into small time frames and the frames are transformed into frequency domain using a WT(Wavelet Transform). We quantize the wavelet coefficients and embedded secret data into the quantized wavelet coefficients. The destination regard quantization errors of received speech as seceret dat. As most speech watermark techniques have a trade off between noise robustness and speech quality, our method also have. However we solve the problem with a partial quantization and a noise level dependent threshold. In additional, we improve the speech quality with de-noising method using wavelet transform. Since the signal is processed in the wavelet domain, we can easily adapt the de-noising method based on wavelet transform. Simulation results in the various noisy environments show that the proposed method is reliable for secret communication.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.303-307
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• 2002

Although Synthetic Aperture Radar(SAR) is a very powerful and attractive tool, automatic interpretation of SAR images is extremely difficult because of several reason. Spatially, speckle noise reduction in SAR images is important step to interpret the SAR image at the preprocessing step. The speckle noise in SAR images is modeled to be multiplicative, and therefore, a signal-dependent noise. So, it has deflated many image-denoising algorithms that are based on additive noise model. In this paper, we propose an adaptive wavelet shrinkage method for speckle noise reduction in SAR images by analyzing the high frequency level in detail. We first decompose minutely the high frequency level to analyze the noise level. And then, we determine the weighting threshold value per the level, and layer. Finally, using those weighting threshold, we produce the efficient wavelet shrinkage method. So, this method not only reduces the speckle noise, but also preserves image detail and sharpness.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.345-346
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• 2006

In this paper, we propose the noise suppressor with the psychoacoustic model and wavelet packet transform. The objective of the scheme is to enhance speech corrupted by colored or non-stationary noise. If corrupted noise is colored, subband approach would be more efficient than whole band one. To avoid serious residual noise and speech distortion, we must adjust the Wavelet Coefficient threshold. In this paper, the subband is designed matching with the critical band. And WCT is adapted by noise masking threshold(NMT) and segmental signal to noise ratio(seg_SNR). Consequently this work improve the PESQ-MOS about 0.23 in the case of coded speech.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.313-316
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• 2000

Wavelet transform has specific properties for image coding. The property used at this Paper is clustering of significant coefficients across subband. These coefficients are classified in significant coefficient and insignificant coefficient on a threshold value, and symbolized EZW decreases symbol-position information using zero-trees, but threshold value fall for raising resolution, then coding cost of significant coefficients is expensive. To avoid this fact, this paper uses quad-tree representing coefficient-position information. a magnitude of significant coefficient is represented on matrix used at EZW. the proposed algorithm is hoped for raising a coding cost.

• Journal of Korea Multimedia Society
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• v.4 no.6
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• pp.500-509
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• 2001

Image compression based on Wavelet gives much better quality than JPEG based on DCT, but suffers from ringing or blurring effects around edges as the compression is increased. In this paper, we proposed enhanced image compression by pre-processing wavelet coefficients. This pre-processing is performed by making a low threshold and enhanced by zerotree scan method when subband's zerotrees are established. It might increase significants coefficient by means of modifying the threshold and reflect on the orientation of subbands. Some experimental results show our method is more efficient than the conventional methods, JPEG. And then the developed coding scheme improves the quality of images and visually shows more pleasing results for most practical images.

• Earthquakes and Structures
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• v.20 no.4
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• pp.457-471
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• 2021

This study aims at providing a simple and effective methodology to define a meaningful characteristic period for special class of earthquake records named "pulse-like ground motions". In the proposed method, continuous wavelet transform is employed to extract the large pulse of ground motions. Then, Fourier amplitude spectra obtained from the original ground motion and the residual motion is simply compared. This comparison permits to define a threshold pulse-period (Tp∗) as the threshold period above which the pulse component has negligible contributions to the Fourier amplitude spectrum. The effect of pulse on frequency content of motions was discussed on the light of this definition. The advantage and superior features of the new definition were related to the inelastic displacement ratio (IDR) for single-degree-of-freedom systems with period equal to one half of the threshold period. Analyses performed for the proposed period at three ductility levels u=2,4,6 were compared with the results obtained at half of pulse period derived from wavelet analysis, peak-point method and the peak of product of the velocity and the displacement response spectra (Sv x Sd). According to the results, pulse effects on inelastic displacement ratio seem to be more important when $\frac{T_p^*}{T}=2$ (T is the fundamental vibration period of system). The results showed that utilizing of the proposed definition could facilitate an enhanced understanding of pulse-like records features.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.499-507
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• 2016

Medical imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultrasound (US) produce a large amount of digital medical images. Hence, compression of digital images becomes essential and is very much desired in medical applications to solve both storage and transmission problems. But at the same time, an efficient image compression scheme that reduces the size of medical images without sacrificing diagnostic information is required. This paper proposes a novel threshold-based medical image compression algorithm to reduce the size of the medical image without degradation in the diagnostic information. This algorithm discusses a novel type of thresholding to maximize Compression Ratio (CR) without sacrificing diagnostic information. The compression algorithm is designed to get image with high optimum compression efficiency and also with high fidelity, especially for Peak Signal to Noise Ratio (PSNR) greater than or equal to 36 dB. This value of PSNR is chosen because it has been suggested by previous researchers that medical images, if have PSNR from 30 dB to 50 dB, will retain diagnostic information. The compression algorithm utilizes one-level wavelet decomposition with threshold-based coefficient selection.