• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.71-76
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• 2023

This paper studied a method of attenuating nonlinear noise using a Wiener filter of a neural network structure in an acoustic noise attenuator. This system improves nonlinear noise attenuation performance with a deep learning algorithm using a neural network Wiener filter instead of using a conventional adaptive filter. A voice is estimated from a single input voice signal containing nonlinear noise using a 128-neuron, 8-neuron hidden layer and an error back propagation algorithm. In this study, a simulation program using the Keras library was written and a simulation was performed to verify the attenuation performance for nonlinear noise. As a result of the simulation, it can be seen that the noise attenuation performance of this system is significantly improved when the FNN filter is used instead of the Wiener filter even when nonlinear noise is included. This is because the complex structure of the FNN filter expresses any type of nonlinear characteristics well.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.113-120
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• 1990

In this paper, the deconvolution method utilizing a modified Wiener filter is applied for the enhancement of lateral resolution of ultrasonic B-scan Images. For this purpose, a phantom composed of wires which are 0.6mm of diameter and apart in the range between 3 to 9mm is constructed. The modified Wiener filter with optimal parameter is applied to the phantom for the analysis of ultrasonic image. The results obtained are as follows'When all parameters of the modified Wiener filter are optimal, the resolution of B-scan images is enhanced by 50 percent : Othenrise, the images are blurred, spilt at peak points, or noises are strengthened severely. When the point-spread function representing the characteristic function of the system is determined, the selection ranges of op- timum parameters may be narrowed. It is expected that the proposed method may be able to apply to clinic situations for more accurate image analysis by means of reducing the loss of important information.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2328-2333
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• 2020

The filter technique was applied to noise images, as noise is the significant factor that cause poor image quality due to lower photon counting. The purpose of this study is to confirm that image quality can be improved using the median modified Wiener filter (MMWF) technique; this is achieved via a National Electrical Manufacturers Association International Electrotechnical Commission body phantom with four large spheres that are filled with the ^99mTc radioisotope when evaluating the image quality. Conventional filters such as Wiener, Gaussian, and median filters were designed, and signal to noise ratio, coefficient of variation, and contrast to noise ratio were used as the evaluation parameters. The improvement in the image quality was in the following order, from the least to the highest improvement, in all cases: Wiener filter, Gaussian filter, median filter, and the MMWF technique. The results show that the image quality was improved from 20.6 to 65.5%, 7.4-40.3%, and 12.7-44.7% for the SNR, COV, and CNR values, respectively, when using the MMWF technique, compared with the use of conventional filters. In conclusion, our results demonstrated that the MMWF technique is useful for reducing the noise distribution in gamma camera images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5C
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• pp.403-412
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• 2008

This paper presents the methods which obtain the solution of Wiener-Hopf equation by LMS algorithm and get the coefficient of TDL filter in lattice filter directly. For this result, we apply an orthogonal input signal generated by lattice filter into an equivalent Wiener-Hopf equation and shows the scheme that can obtain the solution by using the MMSE algorithm. Conventionally, the method like aforementioned scheme can get an error and regression coefficient recursively. However, in this paper, we can obtain an error and the coefficients of TDL filter recursively. And, we make an theoretical analysis on the convergence characteristics of the proposed algorithm. Then we can see that the result is similar to conventional analysis. Also, by computer simulation, we can make sure that the proposed algorithm has an excellent performance.

• International Journal of Internet, Broadcasting and Communication
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• v.4 no.1
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• pp.18-22
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• 2012

In this paper, we propose an equivalent Wiener-Hopf equation. The proposed algorithm can obtain the weight vector of a TDL(tapped-delay-line) filter and the error simultaneously if the inputs are orthogonal to each other. The equivalent Wiener-Hopf equation was analyzed theoretically based on the MMSE(minimum mean square error) method. The results present that the proposed algorithm is equivalent to original Wiener-Hopf equation. In conclusion, our method can find the coefficient of the TDL (tapped-delay-line) filter where a lattice filter is used, and also when the process of Gram-Schmidt orthogonalization is used. Furthermore, a new cost function is suggested which may facilitate research in the adaptive signal processing area.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9C
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• pp.743-748
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• 2008

This paper makes a research on the equivalent Wiener-Hopf equation which can obtain the coefficient of TDL filter on orthogonal input signal in terms of mean square error. Using this result, we can present the coefficient and error of TDL filter directly without inverse orthogonalization process on orthogonal input signal. We make a theoretical analysis on MMSE and show an Wiener-Hopf solution and the proposed equivalent one in mathematical example simultaneously.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.561-564
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• 2001

잡음에 의해 훼손된 영상 신호를 복원할 때 쓰이는 Wiener filter는 국부영역의 잡음 분산과 신호 분산을 가지고 적응적으로 필터의 파라미터를 조절한다. 그러나 기존의 Wiener filter는 고정된 필터 마스크를 사용함으로써, 평탄 영역의 잡음을 크게 제거하면, 에지 부분의 잡음이 살고, 에지 부분의 잡음을 제거하면, 평탄영역의 잡음이 사는 특성이 있다. 본 논문은 Kirsh mask로 에지와 그 방향성을 판별한 후, 에지 부분의 잡음을 제거하면서 평탄 영역의 잡음도 동시에 제거하기 위해 가변적인 필터 마스크를 사용했으며, 잡음에 의해 훼손된 방향성 정보를 살러 주기위해 필터 마tm크와 훼손된 영상 이미지에 방향성 정보를 추가했다. 제안된 방법으로 실험한 결과 주관적 비교에서 에피 부분이 잡음을 제거하고 방향성을 살렸으며, PSNR을 이용한 객관적 비교에서도 기존알고리즘보다 개선된 성능을 보였다.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.293-302
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• 1989

A Dual Sensor Wiener Filter technique was used to improve the image quality of the scanning type digital radiographic system (resolution and SNR). In this method, two images were acquried simultaneously using two sensors with high and low resolution and SNR values. Using the cross Power spectrum between dual sensor outputs of the same chest radiographic image. we design a new type of Wiener Filter and implement it with fast algorithm. We compared the performance of this new dual sensor filter with conventional single sensor filters (Wiener Filter and Parametric Projec- tion Filter) . In simulation studies, it is shown that this new method has SNR improvement of 1-3 dB better than conventional filters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1393-1398
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• 2004

Image is degraded by several causes such as the process of acquisition, storage and transmission. To restore those images, many researches have been continued. Centrally methods to restore degraded image by AWGN(additive white gaussian noise) a.e mean filter and wiener filter. Especially, mean filter is superior in noise reduction of area that is a small change of luminosity. But mean filter brings about the effect smoothing edge components of the image, because it does'nt consider characteristics of the image. So in this paper we propose an image restoration method compounding respective images adding established weights, after filtering with mean filter and powerful wiener filter in both improvement of contrast and preservation of edge components.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.9
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• pp.1293-1298
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• 2013

This paper proposes a noise reduction algorithm using Wiener filter to remove the noise components from the noisy speech in order to improve the speech signal. The proposed algorithm first removes the noise spectrums of white noise from the noisy signal based on the noise reshaping and reduction method at each frame. And this algorithm enhances the speech signal using Wiener filter based on linear predictive coding analysis. In this experiment, experimental results of the proposed algorithm demonstrate using the speech and noise data by Japanese male speaker. Based on measuring the spectral distortion (SD) measure, experiments confirm that the proposed algorithm is effective for the speech by contaminated white noise. From the experiments, the maximum improvement in the output SD values was 4.94 dB better for white noise compared with former Wiener filter.