• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.717-723
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• 2023

Liver disease is highly associated with death, and other abdominal diseases are also important causes affecting a person's lifespan, and a CT scan is essential when treating abdominal diseases. High radiation exposure is essential to create images that are good for reading, but managing the patient's radiation exposure is also essential. In this study, a post-processing wavelet algorithm was proposed to improve the image quality of abdominal CT images. Wavelets have the disadvantage of having to set a threshold value depending on the type of input image. Therefore, we experimentally proposed the threshold value of the wavelet and evaluated whether the image quality was effective. As a result of the experiment, the optimal threshold value for abdominal CT images was calculated to be 50. In the case of image 1, noise was improved by 49% and in the case of image 2, by 29%, and the contrast also increased. if the results of this study are applied for post-processing after abdominal CT, image quality can be improved and it will be helpful in disease diagnosis.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.296-306
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• 2006

We proposed the algorithm for the quantization noise reduction based on variable filter adaptive to edge signal in MPEG postprocessing system. In our algorithm, edge map and local modulus maxima in the decoded images are obtained by using 2D Mallat wavelet tilter. And then, blocking artifacts in inter-block are reduced by Gaussian LPF that is variable to filtering region according to edge map. Ringing artifacts in intra-block are reduced by 2D SAF according to local modulus maxima. Experimental results show that the proposed algorithm was superior to the conventional algorithms as regards PSNR, which was improved by 0.04-0.20 dB, and the subjective image quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.673-682
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• 2004

The adaptive beam-forming is promising approach for noise reduction in hearing aids. This approach has come in the focus of interest only recently, because of the availability of new and powerful digital signal processors. The adaptation U using usually a Least Mean Squares algorithm, updates the weight vector. In this Paper, we propose a fast wavelet based adaptive algorithm using variable step size algorithm which varies adaptive constant by the change of signal environment. We compared the performance of the proposed algorithm with the known adaptive algorithm using computer simulation of multi channel adaptive bemformer in hearing aids. As the result the proposed algorithm is suitable for adaptive signal processing area using hearing aids and has advantages reducing computational complexity. And we show the beam-forming system using proposed algorithm converges stably in a sudden change of system environment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.742-746
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• 2008

With the trend of factory automation, nowadays, much industrial machinery tends to be put into 24-hours operation a day. However, these trends in industrial equipments also increase the possibility of various mechanical problems and bring about innumerable maintenance cost. There is a strong need of the condition monitoring and diagnosis for industrial equipment, especially rotating machinery, since they are connected not only to the reduction in the maintenance costs but also connected to the enhancement of production efficiency. Generally, to evaluate the operating conditions in the machinery in the industrial field, various physical properties are monitored. Among them, vibration and Noise signals are the mist important indicator and it is effectively used in many diagnosis systems for machinery. Much previous research is based in the FFT (Fast Fourier Transform) method. The spectral analysis is assumed that the signal is stationary. However, almost random signals are non-stationary. The wavelet transform has been recognized an efficient Method. Most interesting sounds have time-varying features. Signal processing techniques for the analysis of transient sound have been not clearly given yet.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.1
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• pp.11-17
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• 1998

One of the main techniques for diagnosing heart disease is by examining the electrocardiogram(ECG). Many studies on detecting the QRS complex, p, and T waves have been performed because meaningful information is contained in these parameters. However, the earlier detecting techniques can not effectively extract those parameters from the ECG that is severely contaminated by noise source. In this paper, we performed the extracting parameters from and recovering the ECG signal using wavelets transform that has recently been applying to various fields.

• Journal of Broadcast Engineering
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• v.17 no.4
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• pp.625-633
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• 2012

Extended Depth of Focus (EDF) algorithms for extracting three-dimensional (3D) information from a set of optical image slices are studied by many researches recently. Due to the limited depth of focus of the microscope, only a small portion of the image slices are in focus. Most of the EDF algorithms try to find the in-focus area to generate a single focused image and a 3D depth image. Inherent to most image processing algorithms, the EDF algorithms need parameters to be properly initialized to perform successfully. In this paper, we select three popular transform-based EDF algorithms which are each based on pyramid, wavelet transform, and complex wavelet transform, and study the performance of the algorithms according to the initialization of its parameters. The parameters we considered consist of the number of levels used in the transform, the selection of the lowest level image, the window size used in high frequency filter, the noise reduction method, etc. Through extended simulation, we find a good relationship between the initialization of the parameters and the properties of both the texture and 3D ground truth images. Typically, we find that a proper initialization of the parameters improve the algorithm performance 3dB ~ 19dB over a default initialization in recovering the 3D information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.658-665
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• 2015

In the production of medical images, noise reduction and contrast enhancement are important methods to increase qualities of processing results. By using the edge-based denoising and adaptive nonlinear histogram stretching, a novel medical image enhancement algorithm is proposed. First, a medical image is decomposed by wavelet transform, and then all high frequency sub-images are decomposed by Haar transform. At the same time, edge detection with Sobel operator is performed. Second, noises in all high frequency sub-images are reduced by edge-based soft-threshold method. Third, high frequency coefficients are further enhanced by adaptive weight values in different sub-images. Finally, an adaptive nonlinear histogram stretching method is applied to increase the contrast of resultant image. Experimental results show that the proposed algorithm can enhance a low contrast medical image while preserving edges effectively without blurring the details.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1461-1465
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• 2010

산업기술의 발전은 자연현상에서 발생되는 다양한 형태의 아날로그 신호를 디지털 신호로 변환할 수 있게 되었으나, 자연현상의 신호는 그 정보량이 상당할 뿐만 아니라 디지털 신호로 변환하는 과정에서 발생되는 계통오차 및 동역학적 노이즈 등이 포함되어 있어 신호 해석에 많은 어려움이 있다. 최근 유량조사 분야에서도 최첨단 유량측정 기술인 초음파 유량계 설치를 통하여 기존의 유량조사 방법으로 불가능했던 감조하천과 대하천에서의 실시간 유량자료를 생산하고 있으며, 이에 근거하여 홍수예보 및 하천관리 업무에 효과적으로 활용하고 있다. 본 연구에서는 초음파 유량계가 설치된 여주지점의 2009년도 자동유량측정성과 중 동역학적 노이즈이라 판단되는 신호에 대하여 노이즈저감에 관한 방법론을 검토하고자 한다. 이를 위해 노이즈저감과 시간과 주파수 영역에서 유연한 분해능을 갖는 웨이블릿 변환을 적용하였으며 다양한 웨이블릿 변환함수 중 'db4'를 이용하였다. 여주지점의 자동유량측정성과에 대한 웨이블릿 변환함수 'db4'를 적용한 결과, 30분 이하의 단주기 성분(D1 등)은 동역학적 노이즈으로 판단되었으며, 최종파형분해단계의 근사성분은 원자료에 근사한 결과값을 얻을 수 있었다. 또한, 최종 분해된 자료는 충주조정지댐 방류량과의 상 하류 유출량 분석과 유출률 분석에서 물리적으로 해석이 가능한 결과 값을 얻을 수 있었다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.9
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• pp.24-33
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• 2017

Multitemporal MODIS vegetation index (VI) data are widely used in vegetation monitoring research into environmental and climate change, since they provide a profile of vegetation activity. However, MODIS data inevitably contain disturbances caused by the presence of clouds, atmospheric variability, and instrument problems, which impede the analysis of the NDVI time series data and limit its application utility. For this reason, preprocessing to reduce the noise and reconstruct high-quality temporal data streams is required for VI analysis. In this study, a data reconstruction method for MODIS NDVI is proposed to restore bad or missing data based on the statistical properties of the oscillations in the NDVI temporal dynamics. The first derivatives enable us to examine the monotonic properties of a function in the data stream and to detect anomalous changes, such as sudden spikes and drops. In this approach, only noisy data are corrected, while the other data are left intact to preserve the detailed temporal dynamics for further VI analysis. The proposed method was successfully tested and evaluated with simulated data and NDVI time series data covering Baekdu Mountain, located in the northern part of North Korea, over the period of interest from 2006 to 2012. The results show that it can be effectively employed as a preprocessing method for data reconstruction in MODIS NDVI analysis.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.188-195
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• 1999

The fMRI signals are composed of many various signals. It is very difficult to find the accurate parameter for the model of fMRI signal containing only neural activity, though we may estimating the signal patterns by the modeling of several signal components. Besides the nose by the physiologic motion, the motion of object and noise of MR instruments make it more difficult to analyze signals of fMRI. Therefore, it is not easy to select an accurate reference data that can accurately reflect neural activity, and the method of an analysis of various signal patterns containing the information of neural activity is an issue of the post-processing methods for fMRI. In the present study, fMRI data was analyzed with the Independent Component Analysis(ICA) method that doesn't need a priori-knowledge or reference data. ICA can be more effective over the analytic method using cross-correlation analysis and can separate the signal patterns of the signals with delayed response or motion related components. The Principal component Analysis (PCA) threshold, wavelet spatial filtering and analysis of a part of whole images can be used for the reduction of the freedom of data before ICA analysis, and these preceding analyses may be useful for a more effective analysis. As a result, ICA method will be effective for the degree of freedom of the data.