• Title, Summary, Keyword: Noise Removing

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An Effective Denoising Method for Images Contaminated with Mixed Noise Based on Adaptive Median Filtering and Wavelet Threshold Denoising

  • Lin, Lin
    • Journal of Information Processing Systems
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    • v.14 no.2
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    • pp.539-551
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    • 2018
  • Images are unavoidably contaminated with different types of noise during the processes of image acquisition and transmission. The main forms of noise are impulse noise (is also called salt and pepper noise) and Gaussian noise. In this paper, an effective method of removing mixed noise from images is proposed. In general, different types of denoising methods are designed for different types of noise; for example, the median filter displays good performance in removing impulse noise, and the wavelet denoising algorithm displays good performance in removing Gaussian noise. However, images are affected by more than one type of noise in many cases. To reduce both impulse noise and Gaussian noise, this paper proposes a denoising method that combines adaptive median filtering (AMF) based on impulse noise detection with the wavelet threshold denoising method based on a Gaussian mixture model (GMM). The simulation results show that the proposed method achieves much better denoising performance than the median filter or the wavelet denoising method for images contaminated with mixed noise.

The Study on Removing Random-valued Impulse Noise

  • Yinyu, Gao;Kim, Nam-Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.333-335
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    • 2011
  • In the transmitting process of image processing system, images always be corrupted by impulse noise, especially random-valued impulse noise. So removing the random-valued impulse noise is very important, but it is also one of the most difficult case in image processing. The most famous method is the standard median filter, but at edge, the filter has a special feature which has a tendency to decrease the preserve. As a result, we proposed a filter that detection random-valued impulse noise firstly, next to use efficient method to remove the noise and preserve the details. And through the simulation, we compared with the algorithms and indicated that proposed method significant improvement over many other existing algorithms.

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Speckle noise removing and edge detection in ultrasonic images (초음파 영상에서의 스페클 잡음 제거 및 에지 검출)

  • 원철호;김명남;구성모;조진호
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.4
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    • pp.72-80
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    • 1996
  • In this paper, variable windowing mean filter to remove speckle noise and a measure to detect thin edge in ultrasonic images are proposed. Because ultrasonic images are corrupted by speckle noise showing a granular appearance, good edge detection is difficult. As a result, noise removing filter is needed in preprocessing stage. The speckle noise removing filter is based on mean filter whose window size is changed by the ratio of standard deviation to mean for image signal and noise signal in local area. And the measure expressed the difference of means between tow windows is used for detecting thin edge in filtered image. Results show that variable windowing mean filter removes speckle noise effectively, and proposed measure is useful in detecting thin edge.

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A study on removing the impulse noise using wavelet transformation in detail areas (웨이브렛 상세 영역 변환을 이용한 임펄스 잡음 제거)

  • Cha, Seong-Won;Shin, Jae-Ho
    • Journal of the Korea Society of Digital Industry and Information Management
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    • v.4 no.2
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    • pp.75-80
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    • 2008
  • The impulse noise is very common and typical noise in the digital image. Many methods are invented in order to remove the impulse noise since the development of Digital Image Processing. For example, the median filter has been used for removing the impulse noise. In this paper, we try to remove the impulse noise using wavelet transformation in the wavelet-transformed detail areas. We also compare the algorithm with median filter with the visual and numerical methods. The result using the algorithm in this paper was much better than the median filter in both removing the noise and keeping the edges. The proposed algorithm needs more calculating time but has more advantages than the median filter has.

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Image Restoration Algorithm for Image Noise Removal in Mixed Noise Environment (복합잡음 환경에서 영상 잡음제거를 위한 영상복원 알고리즘)

  • Long, Xu;Kim, Nam-Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.112-114
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    • 2014
  • Generally, images are corrupted by the impulse or AWGN and there are cases where both of these noises are added at once. When it comes to eliminating the noises added to the image, the previous median filter is effective in removing the impulse noise and the average filter is effective for removing AWGN. However, when the complex noises are added, it lacks the noise suppression characteristics, thus in this paper, a non-linear filter algorithm for removing the complex noises was proposed. The simulation results shows the proposed algorithm has excellent de-noising capabilities of compare existing methods.

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Mixed Weighted Filter for Removing Gaussian and Impulse Noise

  • Yinyu, Gao;Kim, Nam-Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.379-381
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    • 2011
  • The image signal is often affected by the existence of noise, noise can occur during image capture, transmission or processing phases. noises caused the degradation phenomenon and demage the original signal information. Many studies are being accomplished to restore those signals which corrupted by mixed noise. In this paper, we proposed mixed weighted filter for removing Gaussian and impulse noise. we first charge the noise type, then, Gaussian is removed by a weighted mean filter and impulse noise is removed by self-adaptive weighted median filter that can not only remove mixed noise but also preserve the details. And through the simulation, we compared with the conventional algorithms and indicated that proposed method significant improvement over many other existing algorithms and can preserve image details efficiently.

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A Study on Removing Impulse Noise using Modified Adaptive Switching Median Filter (변형된 적응 스위칭 메디안 필터를 이용한 임펄스 잡음제거에 관한 연구)

  • Gao, Yinyu;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.15 no.11
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    • pp.2474-2479
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    • 2011
  • 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. In this paper, in order to remove impulse noise we proposed modified adaptive switching median filter that consists of two stages: noise detection and noise removal. Proposed algorithm only processes noise pixels and these noise pixels are replaced by filter output, so proposed algorithm performs well not only removes noise but also preserves edge information. Also we compare existing methods using PSNR(peak signal to noise ratio) as the standard of judgement of improvement effect and choose conventional algorithms to compare with our proposed method.

An Edge-Based Adaptive Method for Removing High-Density Impulsive Noise from an Image While Preserving Edges

  • Lee, Dong-Ho
    • ETRI Journal
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    • v.34 no.4
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    • pp.564-571
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    • 2012
  • This paper presents an algorithm for removing high-density impulsive noise that generates some serious distortions in edge regions of an image. Although many works have been presented to reduce edge distortions, these existing methods cannot sufficiently restore distorted edges in images with large amounts of impulsive noise. To solve this problem, this paper proposes a method using connected lines extracted from a binarized image, which segments an image into uniform and edge regions. For uniform regions, the existing simple adaptive median filter is applied to remove impulsive noise, and, for edge regions, a prediction filter and a line-weighted median filter using the connected lines are proposed. Simulation results show that the proposed method provides much better performance in restoring distorted edges than existing methods provide. When noise content is more than 20 percent, existing algorithms result in severe edge distortions, while the proposed algorithm can reconstruct edge regions similar to those of the original image.

Image Restoration Filter for Preserving High Frequency Components in Impulse Noise Environments (임펄스 잡음 환경에서 고주파 성분을 보존하기 위한 영상 복원 필터)

  • Cheon, Bong-Won;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.4
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    • pp.394-400
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    • 2019
  • Noise removal is one of the required step in processing digital video and there are many researches to develop algorithm that fits with its purpose and environment. However, present impulse noise removal methods are lacking in its function in terms of removing noise in edge and high frequency factors. Therefore, this research has Extended range of masks depending on density to determine noise so that high frequency factors can be preserved. The range of resolution is set based on median and standard deviation of inside resolution after removing impulse noise. afterwards, those resolution within the range are calculated by adding weight to have the final output value. The suggested algorithm has an enhanced function in removing noise in various areas with many edge and high frequency factors than present methods and their functions are compared through simulation.

Abrupt Noise Cancellation and Speech Restoration for Speech Enhancement (음질 개선을 위한 돌발잡음 제거와 음성복원)

  • Son BeakKwon;Hahn Minsoo
    • Proceedings of the KSPS conference
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    • pp.101-104
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    • 2003
  • In this paper, speech quality is improved by removing abrupt noise intervals and then substituting the gaps with estimates of the previous speech waveform. An abrupt noise detection signal has been proposed as a prediction error signal by utilizing LP coefficients of the previous frame. Abrupt noise intervals are estimated by using spectral energy. After removing estimated noise intervals, we applied several waveform substitution techniques such as zero substitution, previous frame repetition, pattern matching, and pitch waveform replication. To prove the validity of our algorithm, the LPC spectral distortion test and the recognition test are executed and, the results show that the speech quality is fairly well improved.

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