• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.217-219
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• 2019

In response to the increased use of digital video device, more researches are actively made on the image processing technologies. Image processing is practically used on various applied fields such as medical photographic interpretation, and object recognition. The types of image noise include Gaussian Noise, Impulse Noise, and Salt and Pepper. Noise refers to the unnecessary information which damages the video and the noise is mainly removed by a filter. Typical noise removal methods are Median Filter and Average Filter. While Median Filter is effective for removing Salt and Pepper noise, the noise removal performance is relatively lower in the environment with high noise density. To address such issue, this study suggested an algorithm which utilizes neighboring pixels to remove noise.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.51 no.3
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• pp.340-347
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• 2015

The Commission for Conservation of Antarctic Marine Living Resources (CCAMLR) is utilized to manage krill resources using acoustic data collection and a scientific observer program operating on the fishing boats. However, the acoustic data were contained seriously noise, example of background, spike, and intermittent noise, due to purpose of fish boats. In this study, the noise removal techniques were confirmed the potential of the acoustic data analysis. Acoustic system and frequency used in the survey were commercial echosounder (ES70, SIMRAD) and 200 kHz split beam transducer. Acoustic data were analyzed using Echoview software (Myriax), and general data analysis and new noise removal method was used. Although a variety of noise, most of the noises have been removed using the noise removal processing. We confirmed the possibility of analyzing the acoustic data obtained from fish boats. The results will be useful for analysis of the acoustic data acquired from krill fishing boats.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.179-181
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• 2009

In this paper, we propose an adaptive noise detection and modified Gaussian removal algorithm using local statistics for impulse noise. In order to determine constraints for noise detection, the local mean, variance, and maximum values are used. In addition, a modified Gaussian filter that integrates the tuning parameter to remove the detected noises. Experimental results show that our method is significantly better than a number of existing techniques in terms of image restoration and noise detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.255-257
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• 2018

Recently, as the frequency of use of video media increases in various fields, the importance of signal processing is increasing. However, many kinds of noise are generated in the transmission and reception process and affect the information of the signal. For this reason, the noise removal is essential as a preprocessing process. In this paper, we propose an algorithm to remove mixed noise of impulse noise and AWGN. The proposed algorithm restores the image through noise determination and pixel change for efficient noise removal. Unlike the conventional method, noise is removed by minimizing both noise effects. Simulation showed excellent noise removal characteristic results were compared and analyzed using the PSNR for such decisions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.675-681
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• 2019

Noise removal is a pre-requisite procedure in image processing, and various methods have been studied depending on the type of noise and the environment of the image. However, for image processing with high-frequency components, conventional additive white Gaussian noise (AWGN) removal techniques are rather lacking in performance because of the blurring phenomenon induced thereby. In this paper, we propose an algorithm to minimize the blurring in AWGN removal processes. The proposed algorithm sets the high-frequency and the low-frequency component filters, respectively, depending on the pixel properties in the mask, consequently calculating the output of each filter with the addition or subtraction of the input image to the reference. The final output image is obtained by adding the weighted data calculated using the standard deviations and the Gaussian distribution with the output of the two filters. The proposed algorithm shows improved AWGN removal performance compared to the existing method, which was verified by simulation.

• Biomedical Engineering Letters
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• v.8 no.4
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• pp.365-371
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• 2018

Uninterrupted monitoring of multiple subjects is required for mass causality events, in hospital environment or for sports by medical technicians or physicians. Movement of subjects under monitoring requires such system to be wireless, sometimes demands multiple transmitters and a receiver as a base station and monitored parameter must not be corrupted by any noise before further diagnosis. A Bluetooth Piconet network is visualized, where each subject carries a Bluetooth transmitter module that acquires vital sign continuously and relays to Bluetooth enabled device where, further signal processing is done. In this paper, a wireless network is realized to capture ECG of two subjects performing different activities like cycling, jogging, staircase climbing at 100 Hz frequency using prototyped Bluetooth module. The paper demonstrates removal of baseline drift using Fast Fourier Transform and Inverse Fast Fourier Transform and removal of high frequency noise using moving average and S-Golay algorithm. Experimental results highlight the efficacy of the proposed work to monitor any vital sign parameters of multiple subjects simultaneously. The importance of removing baseline drift before high frequency noise removal is shown using experimental results. It is possible to use Bluetooth Piconet frame work to capture ECG simultaneously for more than two subjects. For the applications where there will be larger body movement, baseline drift removal is a major concern and hence along with wireless transmission issues, baseline drift removal before high frequency noise removal is necessary for further feature extraction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.183-190
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• 2013

In this paper, we propose a spatially adaptive noise detection and removal algorithm for a single degraded image. Under the assumption that an observed image is Gaussian-distributed, the noise information is estimated by local statistics of degraded image, and the degree of the additive noise is detected by the local statistics of the estimated noise. In addition, we describe a noise removal method taking a modified Gaussian filter which is adaptively determined by filter parameters and window size. The experimental results demonstrate the capability of the proposed algorithm.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.3
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• pp.223-229
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• 2022

This study investigated the methods of effectively removing noises in the acoustic data collected from the cold water zone of the East Sea, and converted that data into NASC values for comparison and analysis. First, the noises accompanying the acoustic data were divided into background noise, impulse noise, transient noise and attenuated signals according to the pattern characteristics. Then, the NASC values before and after noise removal were compared. As a result, the background noises were found to show the highest difference of 6,946 times in the NASC values before and after removal. The attenuated signals showed that the NASC values were higher after the removal.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.125-131
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• 2008

Hyperspectral imaging is used in a wide variety of research since the image is obtained with a wider wavelength range and more bands than multispectral imaging. However, there are limitations, namely that each band has a shorter wavelength range, the computation cost is increased in the case of numerous bands, and a high correlation between each band and noise bands exists. The previous analysis method does not produce ideal results due to these limitations. Therefore, in the case of using the hyperspectral image, image analysis after eliminating noise bands is more accurate and efficient. In this study, noise band elimination of the hyperspectral image preprocessing is highlighted, and we use fractal dimension for noise band elimination. The Triangular Prism Method is used, being the typical fractal dimension method of the curved surface. The fractal dimension of each band is calculated. We then apply the Continuum Removal method to normalize. A total of 35 bands are estimated by noise band with a threshold value that is obtained empirically. The hyperion hyperstpectral image collected on the EO-1 satellite is used in this study. The result delineates that noise bands of the hyperion image are able to be eliminated with the fractal dimension and Continuum Removal method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1551-1557
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• 2019

In modern society, a variety of video media have been widely spread in line with the fourth industrial revolution and the development of IoT technology; in accordance with this trend, numerous researches have been performed to remove noise generated in image and data communications. However, the conventional Additive White Gaussian Noise (AWGN) cancellation techniques are likely to induce a blurring phenomenon in the noise removal process, thus impairing the information of the image. In this study, we propose an algorithm for minimizing the loss of image information in the removal process of AWGN. The proposed algorithm can apply weights according to the characteristics of noise by predicting AWGN in the image, where the output is calculated based on adding and subtracting the outputs of the high pass filter and the low pass filter. Compared to the existing method, the noise reduction using the proposed algorithm exhibited less blurring issues and better noise reduction properties in the AWGN removal process.