• Journal of information and communication convergence engineering
• /
• v.9 no.6
• /
• pp.763-768
• /
• 2011

Image denoising is a lively research field. Today the researches are focus on the wavelet domain especially using wavelet threshold method. We proposed an adaptive threshold method which considering the characteristic of different sub-band, the method is adaptive to each sub-band. Experiment results show that the proposed method extracts white Gaussian noise from original signals in each step scale and eliminates the noise effectively. In addition, the method also preserves the detail information of the original image, obtaining superior quality image with higher peak signal to noise ratio(PSNR).

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.15 no.4
• /
• pp.62-68
• /
• 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 electrical 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, included noise signal in detected PD signal is well eliminated. we can propose the true shine of PD signal.

• Proceedings of the KIPE Conference
• /
• 2015.07a
• /
• pp.463-464
• /
• 2015

이산 웨이블릿 변환(DWT;discrete wavelet transform)의 다해상도 분석(MRA;multi resolution analysis)을 효율적으로 수행하기 위해서는 적절한 모함수(mother wavelet)의 선택이 필수적이다. 본 논문에서는, 노이즈가 포함된 충방전 전압의 디노이징(denoising)을 구현할 때, 모함수에 따른 디노이징 성능을 비교 및 분석한다. 고정된 MRA 레벨에서 6개의 모함수를 비교하되, 각 모함수에서 최대 SNR(signal-to-noise ratio)을 가지는 타입을 대푯값으로 정하여 모함수에 따른 디노이징 성능을 비교한다. 이를 위해, 하드 임계화(hard-thresholding) 및 소프트 임계화(soft-thresholding) 기법을 적용한다.

• Journal of the Korean Institute of Telematics and Electronics T
• /
• v.35T no.3
• /
• pp.37-45
• /
• 1998

In this paper, the different frequency component and time informations from an EMI signal are extracted simultaneously using a wavelet transform and the results of transform in the time and frequency domain are analyzed. Frequencies are extracted from the EMI signal by performing the multiresolution analysis using the Daubechies-4 filter coefficients and the time information through the results of wavelet transform. We have tried the correlation analysis to evaluate the results of wavelet transform. We have chosen the optimal wavelet function for an object signal by comparing the transformed results of various wavelet functions and verified the simulation examples of waveform and harmonic analysis using a wavelet transform. We have proved the denoising effect to the EMI signal using the soft thresholding technique.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.18 no.2
• /
• pp.143-156
• /
• 2014

This paper aims to give a quick view on denoising without comprehensive details. Denoising can be understood as removing unwanted parts in signals and images. Noise incorporates intrinsic random fluctuations in the data. Since noise is ubiquitous, denoising methods and models are diverse. Starting from what noise means, we briefly discuss a denoising model as maximum a posteriori estimation and relate it with a variational form or energy model. After that we present a few major branches in image and signal processing; filtering, shrinkage or thresholding, regularization and data adapted methods, although it may not be a general way of classifying denoising methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.1
• /
• pp.524-527
• /
• 2005

A society has progressed rapidly toward the highly advanced digital information age. However, noise is generated by several causes, when signal is processed. Therefore, methods for eliminating those noises have researched. There were the existing FFT(fast fourier transform) and STFT(short time fourier transform) for removing noise but it's impossible to know information about time and time-frequency localization capabilities have conflictive relationship. Therefore, for overcoming these limits, wavelet-based denoising methods that are capable of multiresolution analysis are applied to the signal processing field. However, existing threshold- and correlation-based denoising methods consider only statistical characteristics for noise, accordingly a lot of noise is acceptable as an edge and are impossible to remove AWGN and impulse noise, at the same time. Hence, in this paper we proposed wavelet-based new denoising algorithm and compared existing methods with it.

• Communications for Statistical Applications and Methods
• /
• v.8 no.1
• /
• pp.109-115
• /
• 2001

Wavelet coefficients are known to have decorrelating properties, since wavelet is orthonormal transformation. but empirically, those wavelet coefficients of images, like edges, are not statistically independent. Jansen and Bultheel(1999) developed the empirical Bayes approach to improve the classical threshold algorithm using local characterization in Markov random field. They consider the clustering of significant wavelet coefficients with uniform distribution. In this paper, we developed wavelet thresholding algorithm using Laplacian distribution which is more realistic model.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.531-532
• /
• 2007

According to the characteristics of power signal, the compression algorithm based on wavelet packet transform and denoising is presented. Its performance is assessed in terms of percentage of zero coefficients and energy retanied in reconstructed signals.

• 전자공학회논문지 IE
• /
• v.43 no.4
• /
• pp.65-75
• /
• 2006

This thesis deals with a wavelet-based method of denoising of infrared images contaminated with impulse noise and Gaussian noise, he method of thresholding the wavelet coefficients using derivatives and median absolute deviations of the wavelet coefficients of the detail subbands was proposed to effectively denoise infrared images with noises. Particularly, in order to eliminate the impulse noise the method of generating binary masks indicating locations of the impulse noise was selected. By this method, the threshold values dividing edges and noises were obtained more effectively proving the validity of the denoising method compared with the conventional wavelet shrinkage method.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.1731-1734
• /
• 2003

In this paper, we describe an approach for image denoising using the lifting construction, with the spatial adaptive wavelet transform. The adaptive lifting scheme is implemented in spatial domain to be adjusted thresholds to reduce noise. In this approach we represent adaptive characteristics of biorthogonal wavelets for choosing predictors effectively. Predict filter is changed from sample to sample according to local signal features with their vanishing moments. We in this approach have implemented and applied to image denoising by finding a relevant minimax threshold. Experimental results show that the adaptive method of denoising process is compared with existing ones, such as non-adaptive wavelet, CRF(13, 7) and SWE(13, 7) wavelets used by JPEG2000.