• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1076-1081
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• 2019

In response to the recent development of IT technologies, there are more demands for visual devices such as display. However, noise is generated in the process of sending video data due to various reasons. Noise is the representative noise which is commonly found. While A-TMF, CWMF, and AMF are the typical ways for removing Salt and Pepper noise, the noise is not removed well in high-density noise environment. To remove the noise in the high-density noise environment, this study suggested an algorithm which identifies whether it's noise or not. If it's not a noise, matches the original pixel. If it's a noise, divide the $3{\times}3$ local mask into the area of the element treated and the area of the element to be processed. Then, algorithm proposes to apply different weights for each element to treat it as an average filter. To analyze the performance of the algorithm, this study compared PSNR to compare the algorithm with other existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.526-532
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• 2022

With the development of IoT technology, image processing is being utilized in various fields such as image analysis, image recognition, medical industry, and factory automation. Noise is generated in image data from causes such as defect in transmission line. Image noise must be removed because it damages the performance of the image processing application program. Salt and Pepper noise is a representative type of image noise, and various studies have been conducted to remove Salt and Pepper noise. Widely known methods include A-TMF, AFMF, and SDWF. However, as the noise density increases, the performance deteriorates. Thus, this paper proposes an algorithm that performs filtering using a fuzzy logic weight mask only in case of noise after noise determination. In order to prove the noise removal performance of the proposed algorithm, an experiment was performed on images with 10% to 90% noise added and the PSNR was compared.

• The Journal of the Acoustical Society of Korea
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• v.29 no.6
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• pp.402-409
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• 2010

In this paper, we propose an efficient integrated suppression algorithm based on combined power of acoustic echo and background noise. The proposed method combines the acoustic echo and noise power by the weighting parameter derived from the decision rule based on the estimated echo to noise power ratio. Therefore, in the proposed approach, the acoustic echo and noise signal are able to be reduced through only one suppression filter based on the estimated combined power. The proposed unified structure improves the problems of the residual echo and noise resulted from the conventional unified structure where the noise suppression (NS) operation is placed after the acoustic echo suppression (AES) algorithm or vice versa. The performance of the proposed algorithm is evaluated by the objective test under various environments and yields better results compared with the conventional scheme.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.347-352
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• 2013

In impulse noise filtering techniques window size play an important role. Usually, an appropriate window is determined according to the noise density. A small window may not be able to suppress noise properly whereas a large window may remove edges and fine image details. Moreover, the value of the central pixel is estimated by considering all pixels within the window. In this work, contrary to the previous approaches, we propose an iterative impulse noise removal scheme that emphasizes on noise-free pixels within a small neighborhood. The iterative process continues until all noisy pixels are replaced with the estimated pixels. In order to estimate the optimal value for a noisy pixel, a genetic programming (GP) based estimator is evolved that takes few noise-free pixels as input. The estimator is constituent of noise-free pixels, arithmetic operators and random constants. Experimental results show that theproposed scheme is capable of removing impulse noise effectively while preserving the fine image details. Especially, our approach has shown effectiveness against high impulse noise density.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.89-98
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• 2000

This paper presents an active noise cancelling model using wavelet transform and subband filter banks based on adaptive filter. The analysis filter banks decompose input and error signals into QMF filter banks of lowpass and highpass bands. Each filter bank uses wavelet filter with dyadic tree structure. The decomposed input and error signals are iterated by adaptive filter coefficients of each subband using filtered-X LMS algorithm. The synthesis filter banks make output signal of wideband with perfect reconstruction to prepare adaptive filter output signals of each subband. The analysis and synthesis niter hants use conjugate quadrature filters for Pefect reconstruction. Also, The delayed LMS algorithm model for on-line identification of error path transfer characteristics is used gain and acoustic time delay factors. The proposed adaptive active noise cancelling modelis suggested by system retaining the computational and convergence speed advantage using wavelet subband filter banks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1886-1891
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• 2017

Denoising which is one of major research topics in the image processing deals with recovering the noisy images. Natural images are well known not only for their local but also nonlocal similarity. Patterns of unique edges and texture which are crucial for understanding the image are repeated over the nonlocal region. In this paper, a nonlocal similarity based denoising algorithm is proposed. First for every blocks of the noisy image, nonlocal similar blocks are gathered to construct a overcomplete data set which are inherently sparse in the transform domain due to the characteristics of the images. Then, the sparse transform coefficients are filtered to suppress the non-sparse additive noise. Finally, the image is recovered by aggregating the overcomplete estimates of each pixel. Performance experiments with several images show that the proposed algorithm outperforms the conventional methods in removing the additive Gaussian noise effectively while preserving the image details.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.277-282
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• 2012

The speech recognition system can not quickly adapt to varied environmental noise factors that degrade the performance of recognition. In this paper, the echo noise robust HMM learning model using average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise HMM learning model consists of the recognition performance is evaluated. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 3.1dB, recognition rate improved as 3.9%.

• The Journal of the Acoustical Society of Korea
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• v.5 no.1
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• pp.17-23
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• 1986

본 논문에서는 FORWARD/BACKWARD 적응 디지털필터를 이용하여 잡음이 섞인 음성의 음질 을 향상하는 방법에 대해 고찰하였다. 이 알고리즘은 음성신호의상관성을 잘 이용하기 위한 현재의 sample을 예측하기 위해 음성신호의 과거 신호뿐만 아니라 미래의 신호도 사용하였다. 이 결과 이 방법 은 백색잡음뿐만 유색잡음의 제거에도 효과적임을 알 수 있었다. 또, 이 방법을 개선한 modified forward/backward 적응 디지털 필터링 방법을 제시하여 성능 향상을 꾀하엿다. 이 개선된 방법은 비교 적 구조가 간단하면서도 여러 종류의 additive noise 에 대해서 잘 동작하며 기존의 방법에 비하여 약 2 유 정도의 개선된 효과를 가져온다.

• Geophysics and Geophysical Exploration
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• v.3 no.3
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• pp.83-87
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• 2000

The noise prediction filter using a local/remote reference was developed to obtain a high quality data from seismic surveys over the area where seismic transmission power is limited. The method used in the noise prediction filter is a 3-layer neural network whose algorithm is backpropagation. A NRF (Noise Reduction Factor) value of about 3.0 was obtained with appling training and test data to the trained noise prediction filter. However, the scaling technique generally used for minimizing EM noise from electric and electromagnetic data cannot reduce seismic noise, since the technique can allow only amplitude difference between two time series measured at the primary and reference sites.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1731-1741
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• 1999

Color images are often corrupted by the noise due to noisy sensors or channel transmission errors. Some filters such as vector media and vector $\alpha$-trimmed mean filter have bee used for color noise removal. In this paper, We propose the fuzzy cluster filters based on the possibilistic c-means clustering, because the possibilistic c-means clustering can get robust memberships in noisy environments. Also, we propose weighted vector $\alpha$-trimmed mean filter to improve the conventional vector $\alpha$-trimmed mean filter. In this filter, the central data are more weighted than the outlying data. In this paper, we implemented the color noise generator to evaluate the performance of the proposed filters in the color noise environments. The NCD measure and visual measure by human observer are used for evaluation the performance of the proposed filters. In the experiment, proposed fuzzy cluster filters in the sense of NCD measure gave the best performance over conventional filters in the mixed noise. Simulation results showed that proposed weighted vector $\alpha$-trimmed mean filters better than the conventional vector $\alpha$-trimmed mean filter in any kinds of noise.