• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.495-503
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• 2000

스트레스 심전계에서 발생되는 근잡음을 제거하기 위하여 wavelet interpolation filter(WIF)를 설계하였다. WIF는 크게 웨이브렛 변환부와 보간법 적용부로 구성되어 있다. 웨이브렛 변환부는 Haar 웨이브렛을 이용하였으며 심전도 저주파 영역과 고주파 영역으로 분할하는 과정이다. 보간법 적용부에서는 분할되어진 신호 중 A3을 선택하여 신호의 재생 성능을 향상시키기 위하여 보간법을 적용하였다. WIF의 성능을 평가하기 위해서 신호대 잡음비, 재생신호 자승오차 및 표준편차의 파라미터를 이용하였다. 본 실험에서는 MIT/BIH 부정맥 데이터베이스, European ST-T 데이터베이스 및 삼각파형을 이용하여 성능 파라미터를 측정하였다. 결과적으로 WIF는 성능 파라미터에서 기존에 많이 사용되고 있는 평균값 필터, 중간값 필터 및 hard thresholding 방법에 비해 우수함을 알 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.156-159
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• 2009

Speckle noise reduction for 3D power doppler ventricle coherent image for restoration and enhancement using wavelet packet transform with separated thresholding is presented. Wavelet Packet Transform divide into low frequency component image to high frequency component image to be multi-resolved. speckle noise is located on high frequency component in multiresolution image mainly. A ventricle image is transformed and inversed with separated threshold function from low to high resolved images for restoration to be utilize visualization for ventricle diagnosis. The experimental result shows that the proposed method has better performance in comparison with the conventional method.

• Journal of computational fluids engineering
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• v.19 no.2
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• pp.58-65
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• 2014

The adaptive wavelet method is studied for the enhancement of computational efficiency of three-dimensional flows. For implementation of the method for three-dimensional Euler equation, wavelet decomposition process is introduced based on the previous two-dimensional adaptive wavelet method. The order of numerical accuracy of an original solver is preserved by applying modified thresholding value. In order to assess the efficiency of the proposed algorithm, the method is applied to the computation of flow field around ONERA-M6 wing in transonic regime with 4th and 6th order interpolating polynomial respectively. Through the application, it is confirmed that the three-dimensional adaptive wavelet method can reduce the computational time while conserving the numerical accuracy of an original solver.

• Journal of the Korea Computer Industry Society
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• v.6 no.2
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• pp.271-278
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• 2005

This paper proposed a new time adapted threshold using the standard deviations of Wavelet coefficients after Wavelet transform by frame scale. The time adapted threshold is set up using the sum of standard deviations of Wavelet coefficient in level 3 approximation and weighted level 1 detail. Level 3 approximation coefficients represent the voiced sound with low frequency and level 1 detail coefficients represent the unvoiced sound with high frequency. After reducing noise by soft thresholding with the proposed time adapted threshold, there are still residual noises in silent interval. To reduce residual noises in silent interval, a detection algorithm of silent interval is proposed. From simulation results, it is demonstrated that the proposed algorithm improves SNR and MSE performance more than Wavelet transform and Wavelet packet transform does.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.1
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• pp.132-144
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• 1997

The rotation-invariant pattern recognition filter using circular harmonic function of the wavelet transforme dsreference image by morlet, mexican-hat, and haar wavelt function is proposed. The rotated reference images, the images sililar to the reference image, and the images which are added by random noise are used for the inpt images, and in case of the input images with random noise, they are applied to the recognition after removing the random noise by the transformed moving average method with proper thresholding value and window size. The proposed optical wavelet circular harmonic matched filter (WCHMF) is a type of the matche dfilter, so that it can be applied to the 4f vander lugt optical correlation system. SNR and discrimination capability of the proposed filter are compared with those of the conventional HF, the POCHF, and the BPOCHF. The proper wavelet function for the reference image used in this paper is achieved by applying morlet, mexican-hat, and harr wavelet function ot the proposed filter, and the proposed filter has good SNR and discrimination capability with rotation-invariance in case of the morlet wavelet function.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.276-283
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• 2021

In this paper, we propose an effective noise reduction method for photon counting imaging using a discrete wavelet transform. Conventional 2D photon counting imaging was used to visualize the object under dark conditions using statistical methods, such as the Poisson random process. The photons in the scene were estimated using a statistical method. However, photons which disturb the visualization and decrease the image quality may occur in the background where there is no object. Although median filters are used to reduce the noise, the noise in the scene remains. To remove the noise effectively, our proposed method uses the discrete wavelet transform, which removes the noise in the scene using a specific thresholding method that utilizes photon counting imaging characteristics. We conducted an optical experiment to demonstrate the denoising performance of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.325-334
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• 2001

It is shown that the multiscale prewhitening matched filter for detecting Gaussian objects in Markov noise can be implemented by the undecimated wavelet transform with a biorthogonal spline wavelet. If the object to be detected is Gaussian shaped and its scale coincides with one of those computed by the wavelet transform, and if the background noise is truly Markov, then optimum detection is realized by thresholding the appropriate details image. Our detection algorithm is applied to the digitized mammograms for detecting microcalcifications. However, microcalcifications are not exactly Gaussian shaped and its background noise may not be Markov. In order to campensate for these discrepancy, Hotelling observer is employed, which is applied to feature vectors comprised of 3-octave wavelet coefficients.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.05b
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• pp.47-53
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• 2004

The paper shows that the combination of the hardware, NI PCI 6110E board and the software, Fourier and continuous wavelet transform(CWT) can be used to implement for extracting the important features of the real-time signal. The results confirmed that CWT produces the fast computation enough for the application of the real-time signal processing except the negligible time delay. In denoising case, because of the lack of translation invariance of wavelet basis, traditional wavelet thresholding leads to pseudo-Gibbs phenomena in the vicinity of discontinuities of signal. In this paper, in order to reduce the pseudo-Gibbs phenomena, wavelet coefficients are threshold and reconstruction algorithm is implement through shift-invariant gibbs free denoising algorithm based on wavelet transform footprint. The proposed algorithm can potentially be extended to more general signals like piecewise smooth signals and represents an effective solution to problems like signal denoising.

• Journal of Korea Multimedia Society
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• v.10 no.9
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• pp.1117-1124
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• 2007

In this paper, an algorithm that can define the threshold value automatically proposed in order to detect a brain ventricle in MRI brain images. After the wavelet transform, edge sharpness, which means the average magnitude of detail signals on the contour of the object, was computed by using the magnitude of horizontal and vertical detail signals. The contours of a brain ventricle were detected by increasing the threshold value repeatedly and computing edge sharpness. When the edge sharpness became maximal, the optimal threshold was determined, and the detection of a brain ventricle was accomplished finally. In this paper, we compared the proposed algorithm with the geodesic active contour model numerically and verified the efficiency of the proposed algorithm by applying real MRI brain images.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.1-10
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• 2000

In this paper, a new digital watermarking algorithm using wavelet transform is proposed. Wavelet transform is widely used for image processing, because of its multiresolution characteristic which conforms to the principles of the human visual system(HVS). It is also very efficient for localizing images in the spatial and frequency domain. Since wavelet coefficients can be characterized by the gaussian distribution, the proposed algorithm uses a gaussian distributed random vector as the watermark in order to achieve invisibility and robustness. After the original image is transformed using DWT(Discrete Wavelet Transform), the coefficients of all subbands including LL subband are utilized to equally embed the watermark to the whole image. To select perceptually significant coefficients for each subband, we use level-adaptive thresholding. The watermark is embedded to the selected coeffocoents, using different scale factors according to the wavelet characteristics. In the process of watermark detection, the similarity between the original watermark and the extracted watermark is calculated by using vector projection method. We analyze the performance of the proposed algorithm, compared with other transform-domain watermarking methods. The experimental results tested on various images show that the proposed watermark is less visible to human eyes and more robust to image compressions, image processings, geometric transformations and various noises, than the existing methods.