• Title/Summary/Keyword: noise removal

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Salt and Pepper Noise Removal using Linear Interpolation and Spatial Weight value (선형 보간법 및 공간 가중치를 이용한 Salt and Pepper 잡음 제거)

  • Kwon, Se-Ik;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.20 no.7
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    • pp.1383-1388
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    • 2016
  • Although image signal processing is used in many fields, degradation takes place in the process of transmitting image data by several causes. CWMF, A-TMF, and AWMF are the typical methods to eliminate noises from image data damaged under salt and pepper noise environment. However, those filters are not effective for noise rejection under highly dense noise environment. In this respect, the present study proposed an algorithm to remove in salt and pepper noise. In case the center pixel is determined to be non-noise, it is replaced with original pixel. In case the center pixel is noise, it segments local mask into 4 directions and uses linear interpolation to estimate original pixel. And then it applies spatial weight to the estimated pixel. The proposed algorithm shows a high PSNR of 24.56[dB] for House images that had been damaged of salt and pepper noise(P = 50%), compared to the existing CWMF, A-TMF and AWMF there were improvements by 16.46[dB], 12.28[dB], and 12.32[dB], respectively.

Development of Lower Noise Excavator (굴삭기 저소음화 기술개발)

  • Ko, Kyung-Eun;Kim, Young-Hyun;Joo, Won-Ho;Kim, Dong-Hae;Bae, Jong-Gug;Shim,, Jae-Koo;Kang, Jeong-Weon;Son, Deuk-Kyun;Kim, Choon-O
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2005.11b
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    • pp.156-160
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    • 2005
  • The radiated noise of the excavator is composed of the various noise sources such as the diesel engine, cooling fan and hydraulic system, so the noise reduction for each noise source is required. In this study, the source contribution analysis for these principal noise sources is performed by using the noise source removal method. And to reduce the noise due to each one, the various experiments and analyses are studied. On the basis of these results, the proper reduction countermeasures are derived to develop the excavator satisfied the $2^{nd}$ noise regulation of EU.

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Morphological Clustering Filter for Wavelet Shrinkage Improvement

  • Jinsung Oh;Heesoo Hwang;Lee, Changhoon;Kim, Younam
    • International Journal of Control, Automation, and Systems
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    • v.1 no.3
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    • pp.390-394
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    • 2003
  • To classify the significant wavelet coefficients into edge area and noise area, a morphological clustering filter applied to wavelet shrinkage is introduced. New methods for wavelet shrinkage using morphological clustering filter are used in noise removal, and the performance is evaluated under various noise conditions.

Extraction of Characteristics of Concrete Surface Cracks

  • Ahn, Sang-Ho
    • Journal of information and communication convergence engineering
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    • v.5 no.2
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    • pp.126-130
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    • 2007
  • This paper proposes a method that automatically extracts characteristics of cracks such as length, thickness and direction, etc., from a concrete surface image with image processing techniques. This paper, first, uses the closing morphologic operation to adjust the effect of light extending over the whole concrete surface image. After applying the high-pass filtering operation to sharpen boundaries of cracks, we classify intensity values of the image into 8 groups and remove intensity values belong to the highest frequency group among them for the removal of background. Then, we binarize the preprocessed image. The auxiliary lines used to measure cracks of concrete surface are removed from the binarized image with position information extracted by the histogram operation. Then, cracks broken by the removal of background are extended to reconstruct an original crack with the $5{\times}5$ masking operation. We remove unnecessary information by applying three types of noise removal operations successively and extracts areas of cracks from the binarized image. At last, the opening morphologic operation is applied to compensate extracted cracks and characteristics of cracks are measured on the compensated ones. Experiments using real images of concrete surface showed that the proposed method extracts cracks well and precisely measures characteristics of cracks.

A Filter Algorithm using Noise Component of Image in Mixed Noise Environments (복합 잡음 환경에서 영상의 잡음 성분을 이용한 필터 알고리즘)

  • Cheon, Bong-Won;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.8
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    • pp.943-949
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    • 2019
  • As use of digital equipment in various fields is increasing importance of processing video and signals is rising as well. However, in the process of sending and receiving signals, noise occurs due to different reasons and this noise bring about a huge influence on final output of the system. This research suggests algorithm for effectively repairing video in consideration to characteristics of its noise in condition where impulse and AWGN noises are combined. This algorithm tries to preserve video features by considering inference to noise components and resolution of filtering mask. Depending on features of input resolution, standard value is set and similar resolutions is selected for noise removal. This algorithm showing simulation result had outstanding noise removal and is compared and analyzed with existing methods by using different ways such as PSNR.

S&P Noise Removal Filter Algorithm using Plane Equations (평면 방정식을 이용한 S&P 잡음제거 필터 알고리즘)

  • Young-Su, Chung;Nam-Ho, Kim
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.27 no.1
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    • pp.47-53
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    • 2023
  • Devices such as X-Ray, CT, MRI, scanners, etc. can generate S&P noise from several sources during the image acquisition process. Since S&P noise appearing in the image degrades the image quality, it is essential to use noise reduction technology in the image processing process. Various methods have already been proposed in research on S&P noise removal, but all of them have a problem of generating residual noise in an environment with high noise density. Therefore, this paper proposes a filtering algorithm based on a three-dimensional plane equation by setting the grayscale value of the image as a new axis. The proposed algorithm subdivides the local mask to design the three closest non-noisy pixels as effective pixels, and applies cosine similarity to a region with a plurality of pixels. In addition, even when the input pixel cannot form a plane, it is classified as an exception pixel to achieve excellent restoration without residual noise.

Estimation of Noise Level in Complex Textured Images and Monte Carlo-Rendered Images

  • Kim, I-Gil
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.1
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    • pp.381-394
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    • 2016
  • The several noise level estimation algorithms that have been developed for use in image processing and computer graphics generally exhibit good performance. However, there are certain special types of noisy images that such algorithms are not suitable for. It is particularly still a challenge to use the algorithms to estimate the noise levels of complex textured photographic images because of the inhomogeneity of the original scenes. Similarly, it is difficult to apply most conventional noise level estimation algorithms to images rendered by the Monte Carlo (MC) method owing to the spatial variation of the noise in such images. This paper proposes a novel noise level estimation method based on histogram modification, and which can be used for more accurate estimation of the noise levels in both complex textured images and MC-rendered images. The proposed method has good performance, is simple to implement, and can be efficiently used in various image-based and graphic applications ranging from smartphone camera noise removal to game background rendition.

Automatic Defect Inspection with Adaptive Binarization and Bresenham's Algorithm for Spectacle Lens Products (적응적 이진화 기법과 Bresenham's algorithm을 이용한 안경 렌즈 제품의 자동 흠집 검출)

  • Kim, Kwang Baek;Song, Dong Heon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.7
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    • pp.1429-1434
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    • 2017
  • In automatic defect detection problem for spectacle lenses, it is important to extract lens area accurately. Many existing detection methods fail to do it due to insufficient minute noise removal. In this paper, we propose an automatic defect detection method using Bresenham algorithm and adaptive binarization strategy. After usual average binarization, we apply Bresenham algorithm that has the power in extracting ellipse shape from image. Then, adaptive binarization strategy is applied to the critical minute noise removal inside the lens area. After noise removal, We can also compute the influence factor of the defect based on the fuzzy logic with two membership functions such as the size of the defect and the distance of the defect from the center of the lens. In experiment, our method successfully extracts defects in 10 out of 12 example images that include CHEMI, MID, HL, HM type lenses.

Support Vector Machine and Improved Adaptive Median Filtering for Impulse Noise Removal from Images (영상에서 Support Vector Machine과 개선된 Adaptive Median 필터를 이용한 임펄스 잡음 제거)

  • Lee, Dae-Geun;Park, Min-Jae;Kim, Jeong-Uk;Kim, Do-Yoon;Kim, Dong-Wook;Lim, Dong-Hoon
    • The Korean Journal of Applied Statistics
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    • v.23 no.1
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    • pp.151-165
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    • 2010
  • Images are often corrupted by impulse noise due to a noise sensor or channel transmission errors. The filter based on SVM(Support Vector Machine) and the improved adaptive median filtering is proposed to preserve image details while suppressing impulse noise for image restoration. Our approach uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a noisy pixel, the improved adaptive median filter is used to replace it. To demonstrate the performance of the proposed filter, extensive simulation experiments have been conducted under both salt-and-pepper and random-valued impulse noise models to compare our method with many other well known filters in the qualitative measure and quantitative measures such as PSNR and MAE. Experimental results indicate that the proposed filter performs significantly better than many other existing filters.