• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.373-376
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• 2009

영상처리 기술은 인간의 시각에 기반을 둔 영상정보와 관련된 분야에서 중요한 기반 기술로써 현재 여러 분야에서 연구가 활발하게 진행 중이다. 여러 응용 분야에서 사용되는 영상처리의 세부 기술범위는 영상 변환, 영상 개선, 영상 복원, 영상 압축등과 같이 다양하며, 이런 영상처리 기술의 중요한 연구 목표 중의 하나는 정확한 정보 추출을 위한 영상정보의 개선에 있다. 영상정보의 개선은 영상의 해석과 인식을 위한 기본적인 과제이며, 영상에서 나타날 수 있는 잡음을 제거하는 영상처리 기술이 영상정보 개선의 한 분야라고 할 수 있다. 영상정보 개선을 위한 기존의 필터링 알고리즘은 잡음제거율이 높은 만큼 경계선의 보존이 어렵다는 단점이 있으며, 이를 보완하기 위해 다른 영상처리 알고리즘을 함께 응용하여 처리함으로써 처리시간이 증가되고 원 영상의 중요한 정보를 훼손할 가능성이 존재한다. 따라서 본 논문에서는 기존의 필터링 알고리즘의 문제점을 개선하는 동시에 잡음 제거율을 높일 수 있는 Fuzzy Mask Filter 알고리즘을 제안한다. Fuzzy Mask Filter 알고리즘은 마스크에서 얻은 정보를 Fuzzy Logic에 적용하여 임계값을 구하며, 구해진 임계값을 기준으로 출력영상의 화소값을 결정하는 알고리즘이다. 본 논문에서 제안한 알고리즘의 효율성을 검증하기 위해 Impulse 잡음과 Salt pepper 잡음을 임의로 생성하여 기존의 알고리즘과 비교한 결과, 제안된 방법이 잡음 영상에 존재하는 픽셀 정보를 훼손하지 않고 잡음을 효과적으로 제거한 것을 확인할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.112-114
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• 2014

Generally, images are corrupted by the impulse or AWGN and there are cases where both of these noises are added at once. When it comes to eliminating the noises added to the image, the previous median filter is effective in removing the impulse noise and the average filter is effective for removing AWGN. However, when the complex noises are added, it lacks the noise suppression characteristics, thus in this paper, a non-linear filter algorithm for removing the complex noises was proposed. The simulation results shows the proposed algorithm has excellent de-noising capabilities of compare existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.817-822
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• 2011

This paper first proposes the speech recognition algorithm by detection of the speech and noise sections at each frame using a neural network training by back-propagation algorithm, then proposes the spectral subtraction method which removes the noises at each frame according to detection of the speech and noise sections. In this experiment, the performance of the proposed recognition system was evaluated based on the recognition rate using various speeches that are degraded by white noise and car noise. Moreover, experimental results of the noise reduction by the spectral subtraction method demonstrate using the speech and noise sections detecting by the speech recognition algorithm at each frame. Based on measuring signal-to-noise ratio, experiments confirm that the proposed algorithm is effective for the speech by corrupted the noise using signal-to-noise ratio.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.664-666
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• 2002

기울기 연산자나 표면접합에 의한 윤곽추출과는 다른 본 논문에서는 확장과 수축방법론을 이용한 윤곽추출 및 잡음제거 방법론을 제안한다. 제안한 알고리즘은 전처리과정을 거친 후 확장과 수축 방법론을 적용함으로써 영상 윤곽 추출률을 높이고자한다 특히, 기존의 윤곽선 추출방법인 소벨과 프리위트알고리즘은 전처리과정인 미디안필터를 거친 후에 사용하였으며, 제안한 방법은 기존의 윤곽선 추출 필터를 거친 후에 사용하였다. 구체적으로 서술하면 확장알고리즘에서는 일정한 임계치를 초과하면 흰색으로 대치하였고, 수축 알고리즘에서는 검정색으로 대치했다. 기존의 잡음제거과정은 윤곽선의 손실은 없었으나 잡음제거가 소량 이루어졌으며, 제안한 방법에서는 약간의 윤곽선 손실을 보였으나 완전하게 잡음을 제거시킬 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2798-2804
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• 2010

The audio and image signal are corrupted by various noises in signal processing, many studies are being accomplished to restore those signals. In this paper, the algorithm is proposed to remove additive Gaussian noise and impulse noise at one dimension signal like an speech signal. The algorithm is composed to remove Gaussian noise after removing impulse noise. And the method using wavelet coefficient accumulation is used to remove the Gaussian noise, and the median filter based on element deviation is applied to remove the impulse noise. Also we compare existing methods using SNR(signal-to-noise ratio) as the standard of judgement of improvemental effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1255-1261
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• 2020

Currently, the spread of mobile devices is gradually increasing. Accordingly, various techniques using images or photos are actively being researched. However, image data generates noise for complex reasons, and the accuracy of image processing increases according to the performance of removing noise. Therefore, noise reduction is one of the essential steps. Salt and pepper noise is a typical impulse noise in the image, and various studies are being conducted to remove the noise. However, existing algorithms have poor noise rejection performance in high frequency areas, and average filters have blurring. Therefore, in this paper, we propose an algorithm that effectively removes salt and pepper noise in the high frequency region as well as the low frequency region using entropy. For objective and accurate judgment of proposed algorithms, MSE and PSNR were used to compare and analyze existing algorithms.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.225-235
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• 2012

Noise reduction is an important issue in the field of image processing because image noise worsens the quality of the input image. The basic difficulty is that the noise and the signal are not easy to distinguish. Simple moothing is one of the most basic and important procedures to remove the noise, however, it does not consider the level of noise. This method effectively reduces the noise but the feature area is simultaneously blurred. This paper considers the block approach to detect noise and image features of the input image so that noise reduction could be adaptively applied. Simulation results show that the proposed algorithm improves the overall quality of the image by removing the noise according to the noise level.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7C
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• pp.413-420
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• 2011

This paper proposes a new adaptive algorithm to cancel the acoustic feedback and noise signals in the binaural hearing aids. The convergence performances of the proposed algorithm are improved by updating coefficients of the feedback canceller after the speech signal is cancelled from the residual signal with dual microphones. The feedback canceller firstly cancels the feedback signal from the microphone signal, and then the noise canceller reduces the noise by the beamforming method. To assure that binaural hearing aids converge stably, the left-sided hearing aid only is converged firstly, next the right-sided hearing aid only is converged. To verify performances of the proposed algorithm, simulations were carried out for a speech. As the results of simulations, it was proved that we can advance 14.43dB SFR(Signal to Feedback Ratio) on the average for the feedback canceller, 10.19dB SNR(Signal to Noise Ratio) improvement on the average for the noise canceller, in case that this algorithm is used.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2001.11b
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• pp.129-133
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• 2001

영상에 포함된 잡음은 화질 및 영상의 압축효율을 저하시킨다. 최근 들어, 영상의 에지 성분을 효율적으로 고려하면서 잡음을 제거하기 위하여 다양한 비정적(nonstationary) 영상 모델에 근거한 잡음제거 알고리즘이 제안되어 왔다. 하지만, 기존의 비정적 영상모델에서는 연산량의 부담을 덜기 위하여 각 화소들 사이에 상관관계(correlation)가 없다는 가정을 하고 있어 영상의 미세한 정보들이 필터링에 의하여 훼손된다. 본 논문에서는 영상의 비정적 상관관계를 고려한 적응적 잡음제거 알고리즘을 제시한다. 영상신호는 비정적 평균을 가진다고 가정되며, 또한 각기 다른 정적(stationary) 상관관계를 가지는 부분 영상으로 분리된다고 가정된다. 제안된 영상 모델에서의 공분산(co-variance) 행렬의 특수한 구조를 이용하여 계산적으로 효율적인 FFT에 기반한 선형 minimum mean square error 필터를 유도한다. 제안된 영상 모델의 정당성 및 알고리즘의 효율성을 제시한다.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.377-382
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• 2012

In this paper, the echo noise robust CHMM learning model using echo cancellation average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise. For improving the performance of a continuous speech recognition, CHMM models were constructed using echo noise cancellation average estimator LMS algorithm. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 1.93dB, recognition rate improved as 2.1%.