• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1368-1375
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• 2013

In this paper, we presents a spatially adaptive noise detection and removal algorithm. Under the assumption that an observed image and the additive noise have Gaussian distribution, the noise parameters are estimated with local statistics, and the parameters are used to define the constraints on the noise detection process, where the first order Markov Random Field (MRF) is used. In addition, an adaptive low-pass filter having a variable window sizes defined by the constraints on noise detection is used to control the degree of smoothness of the reconstructed image. Experimental results demonstrate the capability of the proposed algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.733-736
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• 2003

The denoising for image restoration with wavelet packet and noise variance is presented. The image denoising has the threshold value used absolute average value of noise variance and the translated wavelet packet. The results on the experiment improved over 10% and 5% than the denoising based on wavelet transform and wavelet packet respectively.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.4
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• pp.62-68
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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 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 Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.605-608
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• 2007

The denoising for image restoration based on the Wavelet Packet with absolute average threshold is presented. The Existing method is used standard deviation estimated results in increasing the noise and threshold, and damaging an image quality. In addition that is decreased image restoration PSNR by using the same threshold in spite of changing image because of installing a threshold in proportion of noise size. In contrast, the absolute average threshold with wavelet packet is adapted by changing image to set up threshold by statistic quantity of resolved image and is avoided an extreme impart. The results on the experiment has improved 10% and 5% over than the denoising based on simple wavelet transform and wavelet packet respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1122-1128
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• 2007

The denoising for image restoration based on the Wavelet Packet Transform with AA(Absolute Average) making-threshold is presented. The wavelet packet transform leads to be better in the part of high frequency than wavelet transform to eliminate noise. And the existing threshold determination is used standard deviation estimated results in increasing the noise and threshold, and damaging an image quality. In addition that is decreased image restoration PSNR by using the same threshold in spite of changing image because of installing a threshold in proportion of noise size. In contrast the AA thresholding method with wavelet packet is adapted by changing image to set up threshold by statistic quantity of resolved image and is avoided an extreme impact. The results on the experiment has improved 10% and 5% over than the denoising based on simple wavelet transform and wavelet packet respectively.

• Convergence Security Journal
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• v.24 no.2
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• pp.161-165
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• 2024

Recently, speech recognition models have been advancing, accompanied by the development of various speech processing technologies to obtain high-quality data. In the defense sector, efforts are being made to integrate technologies that effectively remove noise from speech data in noisy battlefield situations and enable efficient speech recognition. This paper proposes a method for effective speech recognition in the midst of diverse noise in a battlefield scenario, allowing commanders to convey orders. The proposed method involves noise removal from noisy speech followed by text conversion using OpenAI's Whisper model. Experimental results show that the proposed method reduces the Character Error Rate (CER) by 6.17% compared to the existing method that does not remove noise. Additionally, potential applications of the proposed method in the defense are discussed.

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.5
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• pp.11-17
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• 2003

It is well known that the double integration of measured acceleration records is one of the most difficult signal processing, particularly in the measurements on civil engineering structures, The measured accelerations of civil engineering structures are usually non-stationary and contain non-gaussian low-frequency noises, which can be significant causes of numerical instabilities in double Integration, For the de-noising of this kind of signals, wavelet transform can be very effective because of its inherent processing features for non-stationary signals, In this paper, the de-noising algorithm for the double integration is proposed using the modified wavelet transform, which is extended version of ordinary wavelet transform to process non-gaussian and low-frequency noises, using the median filter concept, The example studies show that the integration can be improved by the proposed method.

• Journal of the Korean Society of Radiology
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• v.13 no.7
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• pp.941-946
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• 2019

Computed tomography (CT) has been increasing in frequency and indications for use in clinical diagnosis and treatment decisions. Multidetector CT has the advantage of shortening the inspection time and obtaining a high resolution image compared to a single detector CT, but has been pointed out the disadvantage of increasing the radiation exposure. In addition, when the low tube voltage is used to reduce the exposure dose in the CT, noise increases relatively. In the existing method, the method of finding the optimal image quality using the method of adjusting the parameters of the image reconstruction method is not a fundamental measure. In this study, we applied a double-tree complex wavelet algorithm and analyzed the results to maintain the normal signal and remove only noise. Experimental results show that the noise is reduced from 8.53 to 4.51 when using a complex oriented 2D method with 100kVp and 0.5sec rotation time. Through this study, it was possible to remove the noise and reduce the patient dose by using the optimal noise reduction algorithm. The results of this study can be used to reduce the exposure of patients due to the low dose of CT.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.113-122
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• 2016

In this paper, we suggest a approach which comprises fast Fourier transform inversion by wavelet noise attenuation. It represents an inverse filtering by adopting a factor into the Wiener filtering, and the optimal factor is chosen to minimize the overall mean squared error. in order to apply the Wiener filter, we have to compute the power spectrum of original image from the corrupted figure. Since the Wiener filtering contains the inverse filtering process, it expands the noise when the blurring filter is not invertible. To remove the large noises, the best is to remove the noise using wavelet threshold. Wavelet noise attenuation steps are consisted of inverse filtering and noise reduction by Wavelet functions. experimental results have not outperformed the other methods over the overall restoration performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1119-1121
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• 2015

본 논문에서는 트위터 내용을 통해 이벤트를 탐지하는 시스템에서 지명 검출 정확도를 개선하는 방법을 제안한다. SNS를 이용한 개인 정보 유출 사례들이 늘어감에 따라 자신의 위치 정보를 공개하기 꺼려하기 때문에 이벤트가 발생한 지역을 검출하기 위해서는 텍스트 내용을 직접 분석해야한다. 그러나 오타나 줄임말, 동형이의어의 사용으로 정확한 지명 검출에 어려움이 발생하였다. 따라서 정확도를 향상시키기 위해 본 논문에서는 두 가지 지명 검출 기법을 제안한다. 지명 단어에서 발생되는 노이즈를 제거하는 지명 노이즈 제거 기법과 랜드 마크를 이용하여 지명 단어를 확정하는 지명 확정 기법이다. 실험 결과 기존 시스템의 정확도 49%에서 지명 노이즈 제거기법은 56%, 지명 확정 기법은 73%로 각각 향상되었다.