• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.225-235
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• 2012

Noise reduction is an important issue in the field of image processing because image noise worsens the quality of the input image. The basic difficulty is that the noise and the signal are not easy to distinguish. Simple moothing is one of the most basic and important procedures to remove the noise, however, it does not consider the level of noise. This method effectively reduces the noise but the feature area is simultaneously blurred. This paper considers the block approach to detect noise and image features of the input image so that noise reduction could be adaptively applied. Simulation results show that the proposed algorithm improves the overall quality of the image by removing the noise according to the noise level.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.397-400
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• 2016

In digital images, the addition due to noise occurs in the process of obtaining, saving, and transmitting. For examples of noise, there are salt and pepper noise, Gaussian noise, and composition noise where various noises are mixed. Existing filters have insufficient noise removal characteristics because it uses single filters in composite noise environment. Therefore the study suggested a switching filter that processes with special weighted value and median filter according to local mask salt and pepper noise density when central pixel is damaged by salt and pepper noise, and processes by applying weighted values differently according to standard deviation of local mask when damaged by Gaussian noise.

• 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 Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.965-972
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• 2015

Along with the rapid development in digital times, image media are being used in internet, computer and digital camera. But image deterioration occurs due to various exterior reasons in the procedures of acquisition, processing, transmission and recording of digital image and major reason is noise. Therefore in order to remove salt & pepper noise, this study suggested the algorithm which replaces the noise to original pixel in case of non-noise, and processes the noise with Lagrange interpolation method in case of noise. In case high density noise was added and the noise could not be removed, noise characteristics were improved by processing the noises repeatedly. And for objective judgment, this method was compared with existing methods and PSNR(peak signal to noise ratio) was used as judgment standard.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.421-424
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• 2008

This paper proposes an adaptive noise suppression system based on human auditory model to enhance speech signal that is degraded by various background noises. The proposed system detects voiced and unvoiced sections for each frame and implements the adaptive auditory process, then reduces the noise speech signal using neural network including amplitude component and phase component. Base on measuring signal-to-noise ratios, experiments confirm that the proposed system is effective for speech signal that is degraded by various noises.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.47-50
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• 2000

비상관적인 가산잡음에 오염된 음성으로부터 향상된 음성을 얻기 위한 방법 중 Soft Decision에 근거한 음성 향상 기법이 뛰어난 성능을 가진다고 알려져 있다. Soft Decision은 주파수 영역에서 음성에 가산된 잡음을 처리하며, 잡음 환경에 대한 사전정보에 의존적이다. 본 연구에서는 Soft Decision을 근거로 음성에 가산된 잡음신호를 비선형 처리를 하여 효과적으로 음성에 포함된 잡음을 추정하도록 하였으며, 잡음환경에 대한 사전 정보 없이 효율적으로 잡음을 억제하는 방법을 제안한다. 본 연구에서 제안한 음성향상 기법은 주관적인 음질평가에서 기존의 방법들보다 나은 성능을 나타내었다

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2006.06a
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• pp.17-20
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• 2006

본 논문에서는 잡음이 첨가된 연속음성에서의 자동 음절분할을 위해 기존에 사용되고 있는 특징 파라미터인 단구간 에너지 이외에 잡음에 강인한 특성을 가지고 있는 새로운 특징인 스펙트럼 밀도비교척도와 의사역행렬을 이용한 선형판별함수를 제안한다. 기존에 사용되는 단구간 에너지는 잡음이 없는 환경에서는 좋은 성능을 나타내지만 잡음환경에서는 그렇지 못하다. 반면에 논문에서 제안한 척도들은 반대의 성능을 가지므로 주변잡음의 크기에 따라 각각의 파라미터를 적절한 가중치로 조합하는 음절구간 결정함수와 유한상태 머신을 추가로 사용면 무 잡음 환경뿐만 아니라, 잡음이 첨가된 연속음성에서도 일정수준 이상의 음절구간을 분리해 낼 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1215-1220
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• 2016

As society has developed rapidly toward a highly advanced digital information age, a multimedia communication service for acquisition, transmission and storage of image data as well as voice has being commercialized. However, image data is always corrupted by various noises during image processing, so researches for removing noises have been continued until now. There are diverse types of noise on the image including salt and pepper noise, AWGN, and mixed noise. Hence, the filter algorithm for the image recovery was proposed that salt and pepper noise was processed by linear interpolation, histogram weighted values and median filter after defining the noise to lessen the impact of mixed noise added in the image, and AWGN was processed by the pixel information of local mask establishing the weighted values in this study. In addition, the algorithm was compared with the conventional methods for objectively and used the PSNR(peak signal to noise ratio) as the basis of the determination.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1127-1139
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• 2013

In the realization of obtained image by various visual equipments, the addition of noise to the original image is a common phenomenon and the occurrence of the noise is practically impossible to prevent completely. Thus, the noise detection and reduction is an important foundational purpose. In this study, we detect the orientation about feature of images and estimate the level of noise variance based on the measurement of the relative proportion of the noise. Also, we apply the estimated level of noise to the sigma filter on noise reduction algorithm. And using the orientation about feature of images by weighted value, we propose the effective algorithm to eliminate noise. As a result, the proposed statistical noise reduction methodology provides significantly improved results over the usual sigma filtering and regardless of the estimated level of the noise variance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.484-486
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• 2018

Recently, with the increase in use of digital equipment in various fields, the importance of image and signal processing is increasing. However, many types of noise are generated during transmission and reception of digital signal, causing errors. For this reason, noise removal is mandatorily performed during pre-processing phase in many fields. In the present paper, noise is classified through noise evaluation, and noise removal is performed to remove impulse noise and noise with AWGN-added noise. And, proposed is an algorithm which utilizes modified Gaussian filter and directional effective pixels according to noise type. Simulation results show superior noise-removal characteristics, and for objective evaluation, compared with conventional methods.