• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1540-1546
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• 2017

Digital image processing is widely used in various fields including the military, medical, image recognition system, robot and commercial sectors. But in the process of acquiring and transmitting digital images, noise is generated by various external causes. There are various types of general noise depending on the cause and form, but AWGN and impulse noise is one of the leading methods. Removing noise during image processing is essential to the pre-treatment process such as segmentation, image recognition and characteristic extraction. As such, this paper suggests an algorithm that distinguishes the non-edge area and edge area using the Canny edge to apply different filters to different areas in order to effectively remove noise from the image. To verify the effectiveness of the suggested algorithm, it was compared against existing methods using zoom images, edge images and PSNR(peak signal to noise ratio).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.398-401
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• 1997

The EMG signal of spinal cord injured patient is very feeble because that the information from central nervous system is not sufficiently transmitted to molter neuron or muscle fiber. Therefore the observer can not observe contraction and relaxation movement of muscle from the raw EMG signal. In this paper, we propose the muscle contraction and relaxation pattern analysis method of spinal cord injured patient whose EMG signal is composed of the sum of motor unit action potential train with additive white Gaussian noise and impulsive noise. From the EMG model, we denoise impulsive noise using median filter which is a kind of nonlinear filter and the output of median filter is transformed to wavelet transform domain for denoising additive white Gaussian noise using threshold level removal technique. As a result, we can obtain the clear contraction and relaxation pattern.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.765-767
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• 2013

Modern societies need image processing technology as mobile phones, computers and multimedia etc. are supplied, and image signal processing is now applied in many fields. However, images are damaged by impulse noise from various sources; to restore the damaged images from impulse noise standard median filter has been used as a typical method, but it makes errors at edge area lowering image quality. Therefore, in this paper average filter algorithm, in which mask is processed with multiple partition to remove impulse noise, is proposed. Simulation showed that the proposed method is superior in noise removal property to the existing ones.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.268-270
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• 2013

According to the development and supply of Wibro technology digital technology is applied in several fields. Digital images are damaged by various noises in the process of transfer and storage; the image restoration is to reduce the influence of the noises on images by removing the noises. To make good image restoration several methods have been proposed but the noise removal property is not satisfactory. Therefore, to effectively remove noises noise decision is made and if it is decided as a noise, the size of mask is enlarged; this is adaptive median filter algorithm that is proposed in this paper. And through simulation the superiority of this algorithm to existing methods has been verified.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.70-76
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• 1996

In the case of an image corrupted with mixed noise, conventional MMSE filter can not remove such a mixed noise properly, because the impulse moise cause a certain bias of the minimum mean-square error estimate at regions close to outliers. In this paper, we proposed the new method or removal of mixed noise by combining MMSE filtering structure with local multi-windowing method according to directions and with ranked-order method. As a result, the improvement of the image quality with the proposed was obtained between about 9.7 and 35.2 times in the sense of NMSE(normalized mean square errors) evaluation than that of MMSE filter. Also, we could obtain the enhanced image in the mixed noisy image from visual and quantitative aspect.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.186-191
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• 2018

As influence of the 4th industry is growing with development of information society more electronic devices and sensor are used in the field. As this is the case, importance of signal processing during data transfer is rising Furthermore, the need for technology to remove noise caused by various reasons and to stabilize sensor output is growing as well. This research suggests digital filter algorithm that efficiently remove noise by stabilizing output of accelerating sensor. The standard value of this algorithm is calculated by applying Gaussian coefficient. To maintain its feature, final output is obtained by subtracting weight depending on variance from standard value For its evaluation, it is compared with other protocols and its function is checked through output features.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1424-1430
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• 2020

In this paper, we propose an algorithm to remove AWGN by considering the characteristics of noise present in the image. The proposed algorithm uses block matching to calculate the output, and calculates an estimate by determining the similarity between the center mask and the matching mask. The output of the filter is calculated by adding or subtracting the estimated value and the input pixel value, and weighting is given according to the standard deviation of the center mask and the noise constant to obtain the final output. In order to evaluate the proposed algorithm, the simulation was performed in comparison with the existing methods, and analyzed through the enlarged image and PSNR comparison. The proposed algorithm minimizes the effect of noise, preserves important characteristics of the image, and shows the performance of removing noise efficiently.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.195-200
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• 2008

The accuracy of classifying pixels in HIRIS images is usually degraded by noisy bands since noisy bands may deform the typical shape of spectral reflectance. Proposed in this paper is a statistical method for noisy band removal which mainly makes use of the correlation coefficients between bands. Considering each band as a random variable, the correlation coefficient measures the strength and direction of a linear relationship between two random variables. While the correlation between two signal bands is high, existence of a noisy band will produce a low correlation due to ill-correlativeness and undirectedness. The application of the correlation coefficient as a measure for detecting noisy bands is under a two-pass screening scheme. This method is independent of the prior knowledge of the sensor or the cause resulted in the noise. The classification in this experiment uses the unsupervised k-nearest neighbor algorithm in accordance with the well-accepted Euclidean distance measure and the spectral angle mapper measure. This paper also proposes a hierarchical combination of these measures for spectral matching. Finally, a separability assessment based on the between-class and within-class scatter matrices is followed to evaluate the performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2167-2172
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• 2015

Due to the recent progress in information technology, demand for video imaging devices such as displays has grown. In general, images experience deterioration during the process of transmission due to various reasons. Many studies have boon undertaken on ways o reduce such noise. This paper6 suggests an algorithm that makes a judgment on the noise in order to remove the salt & pepper noise and replaces original pixels if they are non-noise while processing noise according to its density. The suggested algorithm shows a high PSNR of 30.49[dB] for Goldhill images that had been damaged of a high density salt & pepper noise(P = 60%), Compared to the exising CWMF, SWMF, and A-TMF, there were improvements by 17.74[dB], 11.52[dB], and 13.76[dB], respectively.