• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.792-794
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• 2014

In the modern society where the communication technology has rapidly developed, image devices such as digital display, camera, etc., forms the center. However, during the transmission of image data, storing, and obtaining, a noise is added to the image due to various reasons and degrades the quality of the image. In this paper, an average filter algorithm modified in order to ease the effect of AWGN(additive white Gaussian noise) being added to the image was proposed. Also compare existing methods through the using PSNR.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1687-1693
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• 2015

Recently, demand for digital image processing devices increases rapidly, more clear images have been required. But, in the process of digital image acquisition, processing and transmission, image degradation occurs due to various external reasons and researches about noise reduction are on the rise. Therefore, this study suggested the algorithm to process AWGN(additive white Gaussian noise) by separately processing as three levels according to the pixel distribution in the mask in order to remove AWGN(additive white Gaussian noise) which is added in the image. Regarding the processed results by applying Barbara images which were damaged by AWGN(σ = 15), suggested algorithm showed the improvement by 2.87[dB], 2.95[dB], 2.88[dB], 1.52[dB], 1.49[dB], 1.58[dB] and 1.25[dB] respectively compared with the existing MF(5 × 5), A-TMF(5 × 5), AWMF(5 × 5), MF(3 × 3), A-TMF(3 × 3), AWMF(3 × 3), GF(5 × 5).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.892-894
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• 2013

Edge has been utilized in various application fields with development of technique of digital image processing. In conventional edge detection methods, there are some methods using mask including Sobel, Prewitt, Roberts and Laplacian operator. Those methods are that implement is simple but generates errors of edge detection in images added AWGN(additive white Gaussian noise). Therefore, to compensate the defect of those methods, in this paper, an edge detection algorithm using modified mask is proposed, and it showed superior edge detection property in AWGN.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.949-956
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• 2014

Recently, with the development of hardware and software technology related with image information delivery, the demand for various multimedia service has increased. But, the process of treating, sending, and storing image signals generates image degradation by various external causes. The main cause of image degradation is noise. image is mostly damaged by AWGN (additive white Gaussian noise). Therefore, there have been active researches on noise elimination. This paper, to reduce the effects of AWGN added to the image, suggests a noise-eliminating algorithm which is excellent in low-frequency and high-frequency characteristics in space. And, this paper, through simulation techniques, compared the result of the suggested algorithm with those of the existing methods. And, to evaluate the performance of it, PSNR (peak signal to noise ratio) was used.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.118-120
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• 2021

In modern society, with the development of IoT technology, various digital equipment is being distributed in a wide range of fields such as CCTV and exploration robots. Accordingly, the importance of data processing is increasing, and various studies are being conducted to remove noise generated in the process of receiving data in the imaging field. Representative noise includes additive white Gaussian noise (AWGN), and existing filters for removing noise include an average filter (AF), an alpha trimmed average filter (A-TAF), and a median filter (MF). However, existing filters have a disadvantage in that they show somewhat insufficient performance in noise removal characteristics in high frequency areas. Therefore, in this paper, in order to effectively remove AWGN existing in the high frequency region, a weight filter according to a distance based on the standard deviation is proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.801-803
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• 2014

In the modern society, image processing is utilized in various fields. Edge detection used for image processing as such is essential for most of the applications. Accordingly, there are studies conducted both in and out of Korea in order to detect edge. Representative edge detection methods include Sobel, Prewitt and Roberts. However, these methods are rather limited when it comes to the edge detection characteristics when used for the image with damaged AWGN(additive white Gaussian noise). Thus, this paper presented edge detection method utilizing local mask in order to overcome the shortcomings of the existing methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.936-939
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• 2015

Recently, the image processing is utilized in various fields and many studies on the image restoration have been carried out in order to remove the noise occurring in the process of data transmission, processing and storage. There are many types of noises added to the image according to the cause and shape, and AWGN(additive white Gaussian noise) is one of typical noises. This paper proposed an algorithm which applies the weighting of filter differently according to the standard deviation in order to alleviate AWGN added to the image, and compared this algorithm with the current methods using PSNR(peak signal to noise ratio) as a criterion of judgment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.684-686
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• 2017

Currently, image processing is used in various fields including military, medical and industrial fields. Noise added to images undermine the quality of images. As such, the removal of noise is an essential step to process images such as through recognition of images, detection of edge and segmentation of images. Studies on removing noise from images are actively being undertaken. One of the leading noises that are added to images is the AWGN(additive white Gaussian noise). This paper suggests an algorithm that synthesizes a filter that uses edge detection and standard deviation to ease AWGN.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.956-958
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• 2016

Currently, image processing has been widely utilized and the noise may be occurred in the processes of image data transmission, processing, and storage. The studies have been actively conducted to eliminate the added noise in the image. The types of noise in the image are various depending on the causes and the forms, and additive white Gaussian noise(AWGN) is the representative one. The algorithm to apply and process the weighted value was suggested by the directions of the pixel in the local mask using edge detection to relieve the added AWGN in the image in this article.