• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.111-118
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• 2004

Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data because of the reasonable performance at the low complexity. The VisuShrink which removes the noise with the universal threshold is one of the techniques. The universal threshold is proportional to the noise deviation and the number of data samples. In general, because the noise deviation is not known, one needs to estimate the deviation for determining the value of the universal threshold. But, only for the finest scale wavelet coefficients, it has been known the way of estimating the noise deviation, so the noise in coarse scales cannot be removed with the VisuShrink. We propose here a new denoising method which removes the noise in each scale except the coarsest scale by Visushrink method. The noise deviation at each band is estimated by the monotonic transform and weighted deviation, the product of estimated noise deviation by the weight, is applied to the universal threshold. By making use of the universal threshold and the Soft-Threshold technique, the noise in each band is removed. The denoising characteristics of the proposed method is compared with that of the traditional VisuShrink and SureShrink method. The result showed that the proposed method is effective in denoising on Gaussian noise and quantization noise.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.1
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• pp.20-26
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• 2013

Image noise reduction in the frequency domain by thresholding is simple, but quite effective. Wavelet domain thresholding has been an active area of research but relatively little work has been published on DCT domain denoising. A novel method for determining the hard threshold for the DCT domain denoising is proposed. The low amplitude DCT coefficients are discarded until the cumulative sum of the discarded signal energy is comparable to that of noise in each DCT block. Cycle spinning is also applied to reduce block artifacts. The proposed method is quite effective and simple enough to be used in portable devices.

• Annual Conference of KIPS
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• 2017.04a
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• pp.960-962
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• 2017

This thesis suggests method that How sound sources used to simulation that can be used to military training and education divide each frequency and each bandwidth filtering method. method for frequency dividing and denoising are suggested into Wavelet analysis. And We materialize authoring tool about filtering that design for wavelet job.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.3
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• pp.84-90
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• 2014

The partial discharge of Cast-resin Transformer has a difficulty to be analyzed, because it is an abnormal condition signal of which stochastic characteristics varies with time variance. In this study, background noise coming from the outside of the cast-resin transformers through ground wire can be removed and only a discharge signal of which defects are simulated can be obtained, using the wavelet transform method, which is a time-frequency domain analysis technique. As a result, it was confirmed that de-noising using the SWT technique is the best efficient among three methods of the wavelet transform techniques.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.178-178
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• 2002

In this paper, we propose an adaptive wavelet method for car interior noise cancellation. For this purpose, we use a node dependent threshold which minimizes the Bayesian risk. We propose a noise estimation method based on spectral entropy using histogram of intensity and a candidate best basis instead of Donoho's best bases. And we modify the hard threshold function. Experimental results show that the proposed algorithm is more efficient, especially to heavy noisy signal than conventional one.

• Journal of the Korean Society of Radiology
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• v.7 no.3
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• pp.181-189
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• 2013

The optimum exposure parameters are found when examined using the automatic mode in FFDM. improve the image quality by applying denoising algorithm and propose methods to reduce AGD(Average Grandular Dose) a patient can receive. For the experiment, Nuclear Associates Model 18-222 phantom was the used, and the entrance dose and AGD were measured. And then, Signal, Noise, SNR and FOM(Figure of Merit) were measured, compared and analyzed image denoising before and after. As the experiment result, first, SNR was the highest at Mo/Mo 23kVp and W/Rh 35kvp was the lowest for the average glandular dose. It showed to use 28kVp of W/Rh to be the best through the result of FOM. SNR was the highest at Mo/Mo 23kVp(image denoising), and it showed to W/Rh and 28kVp to be the best in the FOM result which AGD was considered at the same time. By the image denoising, it is possible to reduce noise while maintain important information in the image.

• Journal of the Korean Magnetic Resonance Society
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• v.6 no.1
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• pp.12-19
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• 2002

A shift-averaged Haar wavelet transform was introduced as a new and excellent tool to distinguish real peaks from the noise contaminated NMR signals. It is based on Haar wavelet transform and translation-invariant denoising process. Donoho＇s universal threshold was newly introduced to the shift-averaged Haar wavelet transform for the purpose of automated noise suppression, and was quantitatively compared with the conventional uniform threshold method in terms or threshold and signal to noise ratio (SNR). New algorithm was combined with a routine to suppress a large solvent peak by singular value decomposition (SVD). Combined algorithm was applied to the real spectrum that containing large solvent peak.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.156-160
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• 2014

Thresholding in frequency domain is a simple and effective noise reduction technique. Determination of the threshold is critical to the image quality. The optimal threshold minimizing the Mean Square Error (MSE) is chosen adaptively in the wavelet domain; we utilize an equation of the MSE for the soft-thresholded signal and the histogram of wavelet coefficients of the original image and noisy image. The histogram of the original signal is estimated through the deconvolution assuming that the probability density functions (pdfs) of the original signal and the noise are statistically independent. The proposed method is quite general in that it does not assume any prior for the source pdf.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.403-417
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• 2016

This paper reviews recent published works dealing with the application of wavelets to image processing based on multiresolution analysis. After revisiting the basics of wavelet transform theory, various applications of wavelets and multiresolution analysis are reviewed, including image denoising, image enhancement, super-resolution, and image compression. In addition, we introduce the concept and theory of quaternion wavelets for the future advancement of wavelet transform and quaternion multiresolution applications.

• Proceedings of the KIEE Conference
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• 2001.07c
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• pp.1679-1681
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• 2001

In this paper, As the wavelet transform has the properties of multi-resolution analysis and time-frequency domain localization, application of wavelet transform is used at partial discharge(PD) signal detected by electromagnetic wave detection method to extract PD signal's various frequency component and its time domain. therefore we can analyzed PD signal's time-frequency domain simultaneously. On the other hand, using wavelet transform denoising process, inclued noise signal in detected PD signal is well elimiated. we can propose the true shape of PD signal.