• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.5
• /
• pp.94-102
• /
• 2007

In de-noising, it is wellknown that wavelet-thresholding algorithm shows near-optimal performances in the minimax sense. However, the wavelet-thresholding algorithm is difficult in realization it on hardware, such as FPGA, because of wavelet function complexity. In this paper, we present a new do-noising technique with the binary tree structured filter bank, which is based on the signal power ratio of each subbands to the total signal power. And we realize it on FPGA. For simple realization, the filter banks are designed by Hadamard transform coefficients. The simulation and hardware experimental results show that the performance of the proposed method is similar with that of soft thresholding de-noising algorithm based on wavelets, nevertheless it is simple.

• The KIPS Transactions:PartB
• /
• v.8B no.6
• /
• pp.693-698
• /
• 2001

Noisy data are often fitted using a smoothing parameter, controlling the importance of two objectives that are opposite to a certain extent. One of these two is smoothness and the other is closeness to the input data. The optimal value of this parameter minimizes the error of the result. This optimum cannot be found exactly, simply because the exact data are unknown. This paper propose the threshold value for noise reduction based on wavelet-thresholding. In the proposed method PSNR results show that the threshold value performs excellently in comparison with conventional methods without knowing the noise variance and volume of signal.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.4 s.304
• /
• pp.127-132
• /
• 2005

The curve of motion indicated to waveform of the fast movement of human extracted using virtual reality or the quantity of time fluctuation of the electromagnetic signal as the quantity of electric fluctuation of the atmosphere is complex. It is important to decide exactly the signal property as the inflection point for the observation signal. When the signal is mixed by noise signal, the traditional method is difficult to detect the inflection point. In this paper the noisy signal is eliminated by wavelet thresholding method and the filter using natural observation theorem is applied. It shows that the inflection point of the signal waveform can be detected exactly.

• Communications for Statistical Applications and Methods
• /
• v.8 no.2
• /
• pp.573-579
• /
• 2001

Wavelet thresholding is a method for he reduction of noise in image. Wavelet coefficients of image are correlated in local characterization. Thee correlations also appear in he original pixel representation of the image, and they do not follow from the characterizations of the wavelet transform. In this paper, we compare noise-free distributions of Bayes approach to improve the classical threshold algorithm.

• Journal of the Korea society of information convergence
• /
• v.6 no.2
• /
• pp.45-49
• /
• 2013

Electrocardiogram(ECG) signal noise as well as conducting other bio-signal measurement were generated. It was intened to enhance the accuracy of cadiac disease diagnosis with removing signal white-noise. Sampling signal was made with generating white-noise. The noise were removed using wavelet transforms and thresholding. Removed noise were compared numerical using SNR(signal to noise ratio). The results compared SNR showed that SURE method was 5.931, 4.9301 in 3, 5dB noise, uninversal was 3.6590, 1.9698 in 7, 9dB noise. De-noising by Thresholding removed noise effectively. ECG signal is expected to improve the accuracy of cadiac desease dianosis.

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

• Journal of Biosystems Engineering
• /
• v.33 no.5
• /
• pp.317-325
• /
• 2008

The electrocardiogram (ECG) measurement system consists of I/O interface to input the ECG signals from two electrodes, FPGA (Field programmable gate arrays) module to process the signal conditioning, and real time module to control the system. The algorithms based on wavelet transform were developed to remove the noise of the ECG signals and to determine the QRS-waves. Triangular wave tests were conducted to determine the optimal factors of the wavelet filter by analyzing the SNRs (signal to noise ratios) and RMSEs (root mean square errors). The hybrid rule, soft method, and symlets of order 5 were selected as thresholding rule, thresholding method, and mother wavelet, respectively. The developed wavelet filter showed good performance to remove the noise of the triangular waves with 10.98 dB of SNR and 0.140 mV of RMSE. The ECG signals from a total of 6 subjects were measured at different measuring postures such as lying, sitting, and standing. The durations of QRS-waves, the amplitudes of R-waves, the intervals of RR-waves were analyzed by using the finite impulse response (FIR) filter and the developed wavelet filter. The wavelet filter showed good performance to determine the features of QRS-waves, but the FIR filter had some problems to detect the peaks of Q and S waves. The measuring postures affected accuracy and precision of the ECG signals. The noises of the ECG signals were increased due to the movement of the subject during measurement. The results showed that the wavelet filter was a useful tool to remove the noise of the ECG signals and to determine the features of the QRS-waves.

• The Journal of the Acoustical Society of Korea
• /
• v.19 no.5
• /
• pp.27-34
• /
• 2000

This paper deals with speech enhancement methods using the wavelet transform. A cycle-spinning scheme and undecimated wavelet transform are used for denoising of speech signals, and then their results are compared with that of the conventional wavelet transform. We apply soft-thresholding technique for removing additive background noise from noisy speech. The symlets 8-tap wavelet and pyramid algorithm are used for the wavelet transform. Performance assessments based on average SNR, cepstral distance and informal subjective listening test are carried out. Experimental results demonstrate that both cycle-spinning denoising(CSD) method and undecimated wavelet denoising(CWD) method outperform conventional wavelet denoising(UWD) method in objective performance measure as welt as subjective listening test. The two methods also show less "clicks" that usually appears in the neighborhood of signal discontinuities.

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

• Journal of Broadcast Engineering
• /
• v.2 no.1
• /
• pp.45-55
• /
• 1997

In this paper, we propose a fast algorithm with adaptive thresholding for the wavelet transform (WT) based blocking artifact reduction. In the fast algorithm, all processings that are equivalent to the processing in WT domain of the first and second scale are performed in spatial domain. In the adaptive thresholding, the threshold values used to classify the block boundary are selected adaptively according to each input image by using the statistical properties of the WT of the coded signal at block boundary and at block center, which can be obtained in spatial domain. Experimental results showed that the proposed fast algorithm is about 10 times faster than the WT-based algorithm. It also was found that the postprocessing with proposed adaptive thresholding yields some PSNR improvement and better subjective quality over that with nonadaptive thresholding which has best performance at high compression ratios of a certain .image, even at low compression ratios.