• Title, Summary, Keyword: Noise Filter

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Adaptive noise cancellation algorithm reducing path misadjustment due to speech signal (음성신호로 인한 잡음전달경로의 오조정을 감소시킨 적응잡음제거 알고리듬)

  • 박장식;김형순;김재호;손경식
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.21 no.5
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    • pp.1172-1179
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    • 1996
  • General adaptive noise canceller(ANC) suffers from the misadjustment of adaptive filter weights, because of the gradient-estimate noise at steady state. In this paper, an adaptive noise cancellation algorithm with speech detector which is distinguishing speech from silence and adaptation-transient region is proposed. The speech detector uses property of adaptive prediction-error filter which can filter the highly correlated speech. To detect speech region, estimation error which is the output of the adaptive filter is applied to the adaptive prediction-error filter. When speech signal apears at the input of the adaptive prediction-error filter. The ratio of input and output energy of adaptive prediction-error filter becomes relatively lower. The ratio becomes large when the white noise appears at the input. So the region of speech is detected by the ratio. Sign algorithm is applied at speech region to prevent the weights from perturbing by output speech of ANC. As results of computer simulation, the proposed algorithm improves segmental SNR and SNR up to about 4 dBand 11 dB, respectively.

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Performance Analysis of Adaptive Extended Kalman Filter in Tracking Radar (추적 레이더에서 적응형 확장 칼만 필터의 성능 분석)

  • Song, Seungeon;Shin, Han-Seop;Kim, Dae-Oh;Ko, Seokjun
    • IEMEK Journal of Embedded Systems and Applications
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    • v.12 no.4
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    • pp.223-229
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    • 2017
  • An angle error is a factor obstructing to track accurate position in tracking radars. And the noise incurring the angle error can be divided as follows; thermal noise and glint. In general, Extended Kalman filter used in tracking radars is designed with considering thermal noise only. The Extended Klaman filter uses a fixed measurement error covariance when updating an estimate state by using ahead state and measurement. But, a noise power varies according to the range. Therefore we purposes the adaptive Kalman filter which changes the measurement noise covariance according to the range. In this paper, we compare the performance of the Extended Kalman filter and the proposed adaptive Kalman filter by considering KSLV-I (Korean Satellite Launch Vehicles).

A Study on Modified Spatial Weighted Filter in Mixed Noise Environments (복합잡음 환경에서 변형된 공간 가중치 필터에 관한 연구)

  • Kwon, Se-Ik;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.1
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    • pp.237-243
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    • 2015
  • In recent image processing, active researches have been made along with rapid development in digital times. However, it is know that the image degradation occurs due to various external factors in the processes of image data processing, transmission and storage, and the main reason of image degradation is due to the noise. Typical methods to remove the noise are CWMF(center weighted median filter), A-TMF(alpha-trimmed mean filter) and AWMF(adaptive weighted median filter) and these methods have a little bit lacking noise reduction characteristics in mixed noise environments. Therefore, in order to remove the mixed noise, image restoration filter processing algorithm was suggested in this paper which processes by applying the median value of the mask and space weighted value after noise judgment. And for the objective judgment, it was compared with existing methods and PSNR(peak signal to noise ratio) was used as a judgment standard.

Reduction of Quantum Noise using Adaptive Weighted Median filter in Medical Radio-Fluoroscoy Image (적응성 가중 메디안 필터를 이용한 의료용 X선 투시 영상의 양자잡음 제거)

  • Lee, Hoo-Min;Nam, Moon-Hyon
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.51 no.10
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    • pp.468-476
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    • 2002
  • Digital images are easily corrupted by noise during the data transmission, data capture and data processing. A technical method of noise analyzing and adaptive filtering for reducing of quantum noise in medical radio-fluoroscopy images is presented. By adjusting the characteristics of the filter according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in diagnosis. We proposed adaptive weighed median(AWM) filters based on local statistics. We showed two ways of realizing the AWM filters. One is a simple type of AWM filter, which is constructed by Homogeneous factor(HF). Homogeneous factor(HF) from the noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the diagnostic systems with various inner statistical properties is proposed. We show by the experimented that the performances of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region. The proposed algorithms were implemented by Visual C++ language on a IBM-PC Pentium 550 for testing purposes and the effects and results of the filter in the various levels of noise and images were proposed by comparing the values of NMSE(normalized mean square error) with the value of the other existing filtering methods.

Speckle noise removing and edge detection in ultrasonic images (초음파 영상에서의 스페클 잡음 제거 및 에지 검출)

  • 원철호;김명남;구성모;조진호
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.4
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    • pp.72-80
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    • 1996
  • In this paper, variable windowing mean filter to remove speckle noise and a measure to detect thin edge in ultrasonic images are proposed. Because ultrasonic images are corrupted by speckle noise showing a granular appearance, good edge detection is difficult. As a result, noise removing filter is needed in preprocessing stage. The speckle noise removing filter is based on mean filter whose window size is changed by the ratio of standard deviation to mean for image signal and noise signal in local area. And the measure expressed the difference of means between tow windows is used for detecting thin edge in filtered image. Results show that variable windowing mean filter removes speckle noise effectively, and proposed measure is useful in detecting thin edge.

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Image Processing for Mixed Noise Removal (복합 잡음 제거를 위한 영상처리)

  • Lee, Kyung-Hyo;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.12
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    • pp.2701-2706
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    • 2009
  • There are Impulse noise and AWGN in a general image processing. Various methods have been proposed to remove these noises. Well-known filters are Mean, Min-max and Median filter and these show different characteristics depending on the noises. When Impulse noise and AWGN are in superposition environment, single filter doesn't remove noises well. Therefore in this paper, we suggested a switching filter using a probability of noise to restore images in this environment. And we compared a filter with conventional method through simulations.

Adaptive Weighted Mean Filter to Remove Impulse Noise in Images (영상에서 임펄스 잡음제거를 위한 적응력 있는 가중 평균 필터)

  • Lee, Jun-Hee;Choi, Eo-Bin;Lee, Won-Yeol;Lim, Dong-Hoon
    • The Korean Journal of Applied Statistics
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    • v.21 no.2
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    • pp.233-245
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    • 2008
  • In this work, a new adaptive weighted mean filter is proposed for preserving image details while effectively suppressing impulse noise. The proposed filter is based on a noise pixel detection-estimation strategy. All the pixels are first detected using an impulse noise detector. Then the detected noise pixels are replaced with the output of the weighted mean filter over adaptive working window according to the rate of corrupted neighborhood pixels, while noise-free pixels are left unaltered. We compare the proposed filter to other existing filters in the qualitative measure and quantitative measures such as PSNR and MAE as well as computation time to verify the capability of the proposed filter. Extensive simulations show that the proposed filter performs better than other filters in impulse noise suppression and detail preservation without increasing of running time.

Hybrid Filter Based on Neural Networks for Removing Quantum Noise in Low-Dose Medical X-ray CT Images

  • Park, Keunho;Lee, Hee-Shin;Lee, Joonwhoan
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.15 no.2
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    • pp.102-110
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    • 2015
  • The main source of noise in computed tomography (CT) images is a quantum noise, which results from statistical fluctuations of X-ray quanta reaching the detector. This paper proposes a neural network (NN) based hybrid filter for removing quantum noise. The proposed filter consists of bilateral filters (BFs), a single or multiple neural edge enhancer(s) (NEE), and a neural filter (NF) to combine them. The BFs take into account the difference in value from the neighbors, to preserve edges while smoothing. The NEE is used to clearly enhance the desired edges from noisy images. The NF acts like a fusion operator, and attempts to construct an enhanced output image. Several measurements are used to evaluate the image quality, like the root mean square error (RMSE), the improvement in signal to noise ratio (ISNR), the standard deviation ratio (MSR), and the contrast to noise ratio (CNR). Also, the modulation transfer function (MTF) is used as a means of determining how well the edge structure is preserved. In terms of all those measurements and means, the proposed filter shows better performance than the guided filter, and the nonlocal means (NLM) filter. In addition, there is no severe restriction to select the number of inputs for the fusion operator differently from the neuro-fuzzy system. Therefore, without concerning too much about the filter selection for fusion, one could apply the proposed hybrid filter to various images with different modalities, once the corresponding noise characteristics are explored.

Enhancement of the Ultrasonic Image Using the Adaptive Window Log Filter for NDI of Aircraft Composite Materials (항공기 복합 재료의 비파괴 검사(NDI)를 위한 가변 창 필터를 이용한 초음파 영상 개선)

  • Hong, G.Y.;Hong, S.B.
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.11 no.2
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    • pp.33-42
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    • 2003
  • In this paper, we propose an enhancement of the ultrasonic image for non-destructive inspection of aircraft composite materials. The ultrasonic images are corrupted by a speckle noise which has the characteristic of granular pattern noise and is in all types of coherent imaging systems, the signal independent and multiplicative noise. In this paper, we derive a filter, called the AWLF(Adaptive Window Log Filter), from the nature of the speckle. The filter is made of the MEAN Filter in the edge region and Log Filter in the flat or noise region. To make a distinction between edge and flat region, we calculate the inclination around the local window instead of computing the local statistics of pixels such as local mean ${\bar{M}}$ and standard deviation ${\sigma}_s$. According to the obtained region, edge region is performed by the mean filter and flat region by the Log filter. Performance of the proposed filter is evaluated by the Enhanced Factor$(F_e)$ and the Speckle Index(SI).

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Noise Reduction by Filter Improvement in Mixed Noise Image (혼재된 잡음 영상내 필터 개선에 의한 잡음제거)

  • Lim, Jae-Won;Kim, Eung-Kyeu
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.5
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    • pp.231-241
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    • 2013
  • In this paper, we propose an average approximation filter which can effectively remove the noises of the images. The noises include impulse noises, gaussian noises and mixed noises. The algorithm is as follows. First, as a step of noise detection, we find whether the difference between the pixel value and the average value is greater than the threshold value or not after getting the average value that removed the minimum and maximum values in the applied mask. If the pixel value is greater than the threshold value, the pixel value is processed as noise. If it is less than or equal to the threshold value, it is processed as non-noise. Next, as the noise reduction step, we output the approximate value in mask as the pixel value and the average value except the minimum and maximum values of the pixel including the noise. As the result of applying this average approximation filter to the mixed noise images, the approximation filter can reduce the noises effectively more than 0.4[dB] as compared with applying the median filter and the average filter, respectively.