• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.280-286
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• 2013

In this paper, a noise reduction system is proposed for the speech interface in intelligent TV applications. To reduce TV speaker sound which are very serious noises degrading recognition performance, a noise reduction algorithm utilizing the direct TV sound as the reference noise input is implemented. In the proposed algorithm, transfer functions are estimated to compensate for the difference between the direct TV sound and that recorded with the microphone installed on the TV frame. Then, the noise power spectrum in the received signal is calculated to perform Wiener filter-based noise cancellation. Additionally, a postprocessing step is applied to reduce remaining noises. Experimental results show that the proposed algorithm shows 88% recognition rate for isolated Korean words at 5 dB input SNR.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.214-216
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• 2019

Recently, as the use of digital equipment increases in various fields, the importance of image and signal processing is increasing. However, many kinds of noise occur in the digital signal during transmission and reception, and this noise greatly affects the final output of the system. In this paper, we propose an algorithm that effectively restores the image by removing noise according to pixel similarity in a mixed noise environment with impulse noise and AWGN. The proposed algorithm sets the reference value according to the noise type and applies the filtering to pixels similar to the reference value to obtain the final output. Simulation results show that the proposed algorithm has good noise canceling performance and compared with conventional methods using PSNR.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.135-140
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• 2013

Noise estimation and detection algorithm to adapt quickly to changing noise environment using the LMS Filter. However, the LMS Filter for noise estimation for a certain period of time and need time to adapt. If the signal changes occur, have the disadvantage of being more adaptive time-consuming. Therefore, noise removal method is proposed to a frame basis AELMS Filter to compensate. In this paper, we split the input signal on a frame basis in noisy environments. Remove the LMS Filter by configuring noise predictions using the mean and variance. Noise, even if the environment changes fast adaptation time to remove the noise. Remove noise and environmental noise and speech input signal is mixed to maintain the unique characteristics of the voice is a way to reduce the damage of voice information. Noise removal method using a frame basis AELMS Filter To evaluate the performance of the noise removal. Experimental results, the attenuation obtained by removing the noise of the changing environment was improved by an average of 6.8dB.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.896-901
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• 2000

차량 번호판 인식 시스템은 크게 번호판 영역의 추출과 인식 단계로 구분된다. 본 논문에서는 전처리단계로써 임계화 방식을 이용하여 번호판 영역을 추출한다. 차량 영상을 임계화하고 영상에서 발생되는 잡음을 제거한다. 잡음이 제거된 차량 영상에서 각 라인의 밀도비율을 계산하여 번호판 영역에서 나타나는 밀도의 비율과 비슷하게 나타나는 영역을 후보영역으로 설정한다. 설정된 후보영역이 번호판 영역의 특징과 유사하게 나타나는 부분을 추출한다. 그리고 추출된 번호판 영역은 코호넨 알고리즘의 2${\times0}$2마스크에 적용시켜서 윤곽선을 추출하고, 번호판의 문자와 숫자를 인식한다. 코호넨 알고리즘의 2${\times0}$2마스크를 이용하게 되면, 윤곽선의 잡음을 최대한으로 줄여주는 특성을 가진다. 잡음이 제거된 후에, 번호판의 문자와 숫자들을 코호넨 알고리즘을 이용하여 인식하였다. 실험 결과에서는 임계화 작업을 이용한 번호판 추출과 코호넨 알고리즘을 이용한 번호판 인식이 우수하는 것을 알 수 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.163-165
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• 2017

본 논문에서는 카메라 잡음 제거에 딥 러닝 알고리즘을 적용하는 연구를 진행하였다. 합성된 가우시언 잡음에 대하여 좋은 잡음 제거 성능을 보이는 DnCNN(Denoising Convolutional Network)를 이용하여 카메라 잡음을 제거하는 학습과 실험을 진행하였으며, 기준 실험으로는 RGB 색공간의 3채널 모두에 대하여 학습한 신경망(Neural Network)을 사용하였고, 본 논문의 실험에서는 그레이 이미지에 대하여 학습한 신경망을 사용하였다. 신경망의 평가를 위하여 딥 러닝 알고리즘 입력 이미지를 RGB 색공간(RGB Color Space)과 YCbCr 색공간(YCbCr Color Space) 2가지 색공간으로 표현하여 사용하였고, 입력 이미지에 노이즈를 첨가하기 위해 가우시안 노이즈(Gaussian Noise)를 이용하였다. 또한 가우시안 잡음과 다른 성질을 갖는 실제 카메라 잡음에 대해서도 학습과 테스트를 진행하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.254-256
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• 2002

기존의 윤곽선 검출윤곽선 검출방법과는 다른 본 논문에서는 효율적인 방법론을 이용해서 윤곽추출 및 잡음제거 방법론을 제안한다. 제안한 방법론은 전처리과정을 거친후 본 방법론을 적용함으로써 영상 윤곽추출률을 높이고자한다. 특히, 기존의 윤곽선 추출방법인 로버트와 라플라 시안방법을 사용한 후에 미디안 필터를 사용했으며, 제안한 방법은 기존의 윤곽선 추출 필터를 거친 후에 사용하였다. 구체적으로 서술하면 일정한 임계치를 초과하면 흰색으로 대치하고, 그렇치 않으면 검정색으로 대치한다. 기존의 잡음제거과정은 윤곽선 손실은 없었으나 잡음제거가 소량 이루어졌으며, 제안한 방법은 약간의 윤곽선 손실을 보였으나 완전하게 잡음을 제거시킬 수 있었다.

• The Journal of the Acoustical Society of Korea
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• v.12 no.5
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• pp.54-63
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• 1993

적응 능동 잡음제거를 위한 새로운 방식을 기술했으며, 잡음원이 존재하는 환경에서 물리적인 잡음 제거를 하는데 목표를 두었다. 기존의 시스템 식별 방식과 달리 적응 예측기를 사용하였으며 신호를 검출하는데 쓰이는 마이크를 하나로 구성하였다. 잡음 신호 자체를 예측함으로써 적극적인 대처를 할 수 있으며 적응 알고리즘은 한번만 사용되고 적응 제어형 시스템 식별 방식에서 필요로 하는 피드백 항을 제거시킴으로써 시스템을 간략화시켰다. 컴퓨터 모의 실험 결과를 통하여 제안한 방식이 주기성을 갖거나 혹은 대역 제한된 잡음에 대해서 매우 유용함을 보였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.722-728
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• 2020

Digital image processing has been applied in a wide range of fields due to the development of IoT technology and plays an important role in data processing. Various techniques have been proposed to remove such noise, but the conventional impulse noise canceling methods are insufficient to remove noise of edge components of an image, and have a disadvantage of being greatly affected by random impulse noise. Therefore, in this paper, we propose an algorithm that effectively removes edge component noise in random impulse noise environment. The proposed algorithm calculates the threshold value by determining the noise level and switches the filtering process by comparing the reference value with the input pixel value. The proposed algorithm shows good performance in the existing method, and the simulation results show that the noise is effectively removed from the edge of the image.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.555-564
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• 2014

The boundary noise in image syntheses using DIBR consists of noisy pixels that are separated from foreground objects into background region. It is generated mainly by edge misalignment between the reference image and depth map or blurred edge in the reference image. Since hole areas are generally filled with neighboring pixels, boundary noise adjacent to the hole is the main cause of quality degradation in synthesized images. To solve this problem, a new boundary noise removal algorithm using a preprocessing of the depth map is proposed in this paper. The most common way to eliminate boundary noise caused by boundary misalignment is to modify depth map so that the boundary of the depth map can be matched to that of the reference image. Most conventional methods, however, show poor performances of boundary detection especially in blurred edge, because they are based on a simple boundary search algorithm which exploits signal gradient. In the proposed method, a two-step hierarchical approach for boundary detection is adopted which enables effective boundary detection between the transition and background regions. Experimental results show that the proposed method outperforms conventional ones subjectively and objectively.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.555-572
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• 2018

Noise reduction processes that reduce or eliminate noise (caused by a variety of reasons) in noise contaminated image is an important theme in image processing fields. Many studies are being conducted on noise removal processes due to the importance of distinguishing between noise added to a pure image and the unique characteristics of original images. Adaptive filter and sigma filter are typical noise reduction filters used to reduce or eliminate noise; however, their effectiveness is affected by accurate noise estimation. This study generates a distribution of noise contaminating image based on a Dirichlet normal mixture model and presents a Bayesian approach to distinguish the characteristics of an image against the noise. In particular, to distinguish the distribution of noise from the distribution of characteristics, we suggest algorithms to develop a Bayesian inference and remove noise included in an image.