• Journal of the Korean Society for Railway
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• v.13 no.3
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• pp.283-289
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• 2010

Recent advance of digital camera results in that image signal processing techniques are widely adopted to railroad security management. However, due to the nature of railroad management many images are acquired in low light level environment such as night scenes. The lack of light causes lots of noise in the image, which degrades image quality and causes errors in the next processes. 3D noise reducing techniques produce better results by using consecutive sequence of images. On the other hand, they cause degradation such as motion blur if there are motions in the sequence. In this paper, we use an adaptive weight filter to estimate more accurate motions and use the result of the adaptive filter to 3D result to improve objective and subjective mage quality.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.55-60
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• 2018

This paper shows the initial results of a study applying deep learning technology in power line communication. In this paper, we propose a system that effectively removes noise by applying a deep learning technique to eliminate noise, which is a cause of reduced power line communication performance, by adding a deep learning model at the receive part. To train the deep learning model, it is necessary to store the data. Therefore, it is assumed that the existing data is stored, and the proposed system is simulated. we compare the theoretical result of the additive white Gaussian noise channel with the bit error rate and confirm that the proposed system model improves the communication performance by removing the noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.253-255
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• 2022

Thanks to the 4th industrial revolution and the development of various communication media, technologies such as artificial intelligence and automation are being grafted into industrial sites in various fields, and accordingly, the importance of data processing is increasing. Image noise removal is a pre-processing process for image processing, and is mainly used in fields requiring high-level image processing technology. Various studies have been conducted to remove noise, but various problems arise in the process of noise removal, such as image detail preservation, texture restoration, and noise removal in a special area. In this paper, we propose a switching mask filter based on the noise intensity to preserve the detailed image information during the impulse noise removal process. The proposed filter algorithm obtains the final output by switching to the extended mask when it is determined that the density is higher than the reference value when noise is determined in the area designated as the filtering mask. Simulation was conducted to evaluate the performance of the proposed algorithm, and the performance was analyzed compared to the existing method.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.465-468
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• 2005

심전도 신호의 잡음 신호는 일정한 주파수대역에 존재하지 않고 측정자의 신체 및 환경조건에 따라서 잡음의 종류와 정도가 다르다. 따라서 기존의 고정 주파수 특성을 갖고 있는 필터로는 효율적인 잡음 제거가 불가능하다. 그래서 본 논문에서는 상황인식을 통해 잡음의 형태를 파악하여 적응적으로 필터를 재구성하는 적응적 잡음제거기술을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.732-735
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• 2012

본 논문은 지상에서 발생하는 전자파 잡음 신호의 유입에 의해 많은 영향을 받을 수 있는 헬기 등 항공기의 레이더경보수신기(Radar Warning Receiver)에서도 레이더 펄스 신호를 탐지할 수 있는 통신전자파잡음 동적제거 기법을 제안하였다. 본 논문은 지상의 노이즈 신호를 분류하는 방법을 제시하였고, 노이즈 신호 레벨을 판단하여 효과적으로 잡음을 제거하는 알고리즘을 제안하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.369-372
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• 2012

대사성 골 질환인 골다공증(Osteoporosis)의 조기 진단을 위한 골 밀도를 측정하는 방법이 최근 연구되고 있다. 골 밀도 영상은 이중 에너지 X 선 흡수법에 의해 측정되는데, 영상에 존재하는 잡음은 뼈 영역 추출과 골 밀도 계산에 어려움을 주고 있다. 따라서 본 논문에서는 최근 신호처리 분야에서 폭넓게 사용되고 있는 sparse 표현을 도입하여 X 선 영상의 잡음을 제거하는 방법을 제안한다. 실험을 통해 제안한 잡음 제거 방법의 결과가 기존의 방법에 비해 개선됨을 MSR(Mean to Standard deviation Ratio)과 CNR(Contrast to Noise Ratio)을 통해 확인하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.29-33
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• 2022

In this paper, we propose the deep learning based pre interference cancellation scheme algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information by applying a deep learning model at the transmitter. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.128-137
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• 2012

When a CCD camera acquires images in the low light level environment, not only the image signals but also noise components are amplified by the AGC (auto gain control) circuit. Since the noise level in the images acquired in the dark is very high, it is difficult to remove noise with existing denoising algorithms that are targeting the images taken in the normal light condition. In this paper, we proposed an adaptive denoising algorithm that can efficiently remove significant noises caused by the low light level. First, the window including a target pixel is transformed to the frequency domain. Then the algorithm compares the characteristics of equally divided four frequency bands. Finally the noises are adaptively removed according to the frequency characteristics. The proposed algorithm successfully improves the quality of low light level images than the existing algorithms do.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.389-396
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• 1997

Several types of electrical artifact seen on electroencephalogram( EEG) records are described. Those are the EOG and the PVC roller pump noise, and so on. An adaptive digital filtering of the electroencephalogram( EEG) is a successful way of suppressing mains interference, but it affects some of the frequency components of the signal, whore artifacts may not be acceptable in some cafes of automatic EEG processing. Thus we studied the method for cancelling these artifacts. This proposed method does not use the reference channel, and is realized by connecting the linear predictor and the fixed FIR filter for the EOG artifact, and by cascading the linear predictor and the noise canceller for the pump artifact. The simulation results illustrate the performances of the proposed method in terms of the capability of interferences suppression. In the results we obtained about 20 dB noise reduction.

• 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.