• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1320-1326
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• 2018

Digital video equipment is important in the 4th industrial revolution and is widely used in different fields for various purpose. Data of digital video equipment is exposed to noise due to different reasons including user environment and processing and such noise affect output and processing method. This can even cause error, resulting in decreased reliability of the equipment. In this research, it offers algorithm to effectively recover video by removing noise and impulse noise occurring during the process of channel delivery. This proposed algorithm recovers video by exploring valid pixel using directional local mask and noise determination. Then, valid pixel calculated goes through the final output calculation through comparative analysis on estimation. For comparing suggested method and algorithm, simulation is carried out. For checking the function of it, PSNR and profile are analyzed.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.702-707
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• 2004

This article is concerned with container pose estimation using CCD a camera and a range sensor. In particular, the issues of characteristic point extraction and image noise reduction are described. The Euler-Lagrange equation for gaussian and random noise reduction is introduced. The alternating direction implicit(ADI) method for solving Euler-Lagrange equation based on partial differential equation(PDE) is applied. The vertex points as characteristic points of a container and a spreader are founded using k order curvature calculation algorithm since the golden and the bisection section algorithm can't solve the local minimum and maximum problems. The proposed algorithm in image preprocess is effective in image denoise. Furthermore, this proposed system using a camera and a range sensor is very low price since the previous system can be used without reconstruction.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.296-306
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• 2006

We proposed the algorithm for the quantization noise reduction based on variable filter adaptive to edge signal in MPEG postprocessing system. In our algorithm, edge map and local modulus maxima in the decoded images are obtained by using 2D Mallat wavelet tilter. And then, blocking artifacts in inter-block are reduced by Gaussian LPF that is variable to filtering region according to edge map. Ringing artifacts in intra-block are reduced by 2D SAF according to local modulus maxima. Experimental results show that the proposed algorithm was superior to the conventional algorithms as regards PSNR, which was improved by 0.04-0.20 dB, and the subjective image quality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.474-480
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• 2020

With the advent of the Fourth Industrial Revolution, many new technologies are being utilized. In particular, video signals are used in various fields. However, when transmitting and receiving video signals, salt and pepper noise and additive white Gaussian noise (AWGN) occur for multiple reasons. Failure to remove such noise when performing image processing can cause problems. Generally, filters such as CWMF, MF, and AMF remove noise. However, these filters perform somewhat poorly in the high-density noise domain and cause smoothing, resulting in slightly lower retention of the edge components. In this paper, we propose an algorithm by effectively eliminating salt and pepper noise using a modified weight filter using standard deviation. In order to prove the noise reduction performance of the proposed algorithm, we compared it with the existing algorithm using PSNR and magnified images.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.151-156
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• 2010

This paper is a study on image noise reduction of modified sigma filter by image decomposition using directivity. Conventional sigma filter has been shown to be a good solution both in terms of filtering accuracy and computational complexity. However, the sigma filter does not preserve well small edges especially for high level of additive noise. In this paper, we propose here a new method using a modified sigma filter. In our proposed method the input image is first decomposed in two components that have features of horizontal, vertical and diagonal direction. Then, two components are applied HPF and LPF. By applying a conventional sigma filter separately on each of them, the output image is reconstructed from the filtered components. Added noise is removed and our proposed method preserves the edges from the image. Comparative results from experiments show that the proposed algorithm achieves higher gains, on average, 2.6 dB PSNR than the sigma filter and 0.5 dB PSNR than the modified sigma filter. When relatively high levels of noise added, the proposed algorithm shows better performance than two conventional filters.

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.149-157
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• 2016

In this paper, we propose a new clutter reduction algorithm, which rejects heavy clutter density in shallow water environments, based on a clustering method. At first, it applies the density-based clustering to active sonar measurements by considering speed of targets, pulse repetition intervals, etc. We assume clustered measurements as target candidates and remove noise, which is a set of unclustered measurements. After clustering, we classify target and clutter measurements by the validation check method. We evaluate the performance of the proposed algorithm on synthetic data and sea-trial data. The results demonstrate that the proposed algorithm provides significantly better performances to reduce clutter than the conventional algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.10
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• pp.939-944
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• 2004

The numerical methods including the performance analysis and the noise prediction of the centrifugal compressor impeller are coupled with the optimization design skill, which consists of response surface method, statistical approach, and genetic algorithm. The flow-field Inside of a centrifugal compressor is obtained numerically by solving Wavier-Stokes equations. and then the propagating noise is estimated from the distributed surface pressure by using Ffowcs Williams-Hawkings formulation. The quadratic response surface model with D-optimal 3-level factorial experimental design points is constructed to optimize the impeller geometry for the advanced centrifugal compressor. The statistical analysis shows that the quadratic model exhibits a reasonable fitting quality resulting in the impeller blade design with high performance and low far-field noise level. The influences of selected design variables, objective functions, and constraints on the impeller performance and the impeller noise are also examined as a result of the optimization process.

• Journal of Korean Society of Disaster and Security
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• v.16 no.3
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• pp.45-56
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• 2023

In this study, the application of ANC (Active Noise Control) technology to address inter-floor noise was explored. To achieve this, an ANC system was developed to manage the heavy impact sound within the frequency range of 40 to 500 Hz. The ANC system utilized an adaptive filter employing a feedforward approach based on the Fx-LMS algorithm. To set up the ANC system, a comprehensive analysis of various variables within the system was performed using computational simulations. This process enabled the identification of optimal filter settings and system configuration arrangements. In addition, the ANC system was implemented in the inter-floor noise test room at the Korea Conformity Laboratories (KCL). Through a certified standard testing procedure, it was confirmed that the ANC system led to a 4 dB reduction in inter-floor noise when the system was activated compared to when it was turned off. The results of this study indicate that the developed ANC system has an effect significant enough to elevate the rating criteria by one level for heavy impact sound.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.8-13
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• 2010

Shape form focus (SFF) is a technique that reconstructs 3D shape of an object using image focus. Although many SFF methods have been proposed, there are still notable inaccuracy effects due to noise and non-optimization of image characteristics. In this paper, we propose a noise filter technique for noise reduction and genetic algorithm (GA) for focus measure optimization. The proposed method is analyzed with a statistical criteria such as Root Mean Square Error (RMSE) and correlation.