• Title, Summary, Keyword: Denoising

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Multiple Decision Model for Image Denoising in Wavelet Transform Domain (웨이블릿 변환 영역에서 영상 잡음 제거를 위한 다중 결정 모델)

  • 엄일규;김유신
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.29 no.7C
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    • pp.937-945
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    • 2004
  • A binary decision model which is used to denoising has demerits to measure the precise ratio of signal to noise because of only a binary classification. To supplement these demerits, complex statistical model and undecimated wavelet transform are generally exploited. In this paper, we propose a noise reduction method using a multi-level decision model for measuring the ratio of noise in noisy image. The propose method achieves good denoising performance with orthogonal wavelet transform because the ratio of signal to noise can be calculated to multi-valued form. In simulation results, the proposed denoising method outperforms 0.1dB in the PSNR sense than the state of art denoising algorithms using orthogonal wavelet transform.

Effective Parameter Estimation of Bernoulli-Gaussian Mixture Model and its Application to Image Denoising (베르누이-가우스 혼합 모델의 효과적인 파라메터 추정과 영상 잡음 제거에 응용)

  • Eom, Il-Kyu;Kim, Yoo-Shin
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.42 no.5
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    • pp.47-54
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    • 2005
  • In general, wavelet coefficients are composed of a few large coefficients and a lot of small coefficients. In this paper, we propose image denoising algorithm using Bernoulli-Gaussian mixture model based on sparse characteristic of wavelet coefficient. The Bernoulli-Gaussian mixture is composed of the multiplication of Bernoulli random variable and Gaussian mixture random variable. The image denoising is performed by using Bayesian estimation. We present an effective denoising method through simplified parameter estimation for Bernoulli random variable using local expected squared error. Simulation results show our method outperforms the states-of-art denoising methods when using orthogonal wavelets.

An Image Denoising Algorithm Using Multiple Images for Mobile Smartphone Cameras (스마트폰 카메라에서 다중 영상을 이용한 영상 잡음 제거 알고리즘)

  • Kim, Sung-Un
    • The Journal of the Korea institute of electronic communication sciences
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    • v.9 no.10
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    • pp.1189-1195
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    • 2014
  • In this study we propose an image denoising algorithm which manipulates the information obtained from multiple images in the same environment for mobile smart phones. We also envisage a multiple images registration method for mobile smart phone cameras equipped with limited computing ability and present an effective image denoising algorithm combining and manipulating the information obtained from multiple images. We proved that the proposed algorithm has much better PSNR value than the method applying single image. We verified that the propose approach has good denoising quality and can be utilized in the feasible level speed on Android smart phones.

A Study on No-line Filter for Image Denoising (영상 잡음제거를 위한 비선형 필터에 관한 연구)

  • Long, Xu;Kim, Nam-Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.411-413
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    • 2013
  • Image signal processing is applied in different areas due to diffusion of smart phone, computer, multimedia etc. However, image most is damaged by impulse noise, and the need of denoising technology for improvement of image quality is coming to the fore. The existing methods for denoising such as mean filter and median filter, but they represent poor denoising. Therefore, the removes impulse noise, this paper proposed the modified mean filter algorithm using standard deviation, and as a simulation result, the proposed method showed excellent denoising capabilities to the existing methods.

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An Improved Weighted Filter for AWGN Removal (AWGN 제거를 위한 개선된 가중치 필터)

  • Long, Xu;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.17 no.5
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    • pp.1227-1232
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    • 2013
  • Recently, the expectation of quality about images over the increasing demand of digital devices is increasing with the development of the technology of the digital. But the images are degraded by a variety of causes, and the main reason is the noises. Therefore, the necessity of denoising comes to the fore, and the research for denoising is progressing dynamically. The images are mainly degraded by AWGN(additive white Gaussian noise), and the characteristics of denoising of existing methods such as mean filter are insufficient. In this paper, an algorithm combined by the spatial weighted filter and the modified adaptive weighted filter is proposed in order to effectively remove the AWGN. In the simulation result, the proposed algorithm showed excellent denoising capabilities.

A Study on Threshold-based Denoising by UDWT (UDWT을 이용한 경계법에 기초한 노이즈 제거에 관한 연구)

  • 배상범;김남호;류지구
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • pp.77-80
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    • 2001
  • This paper presents a new threshold-based denoising method by using undecimated discrete wavelet transform (UDWT). It proved excellency of the UDWT compared with orthogonal wavelet transform (OWT), spatia1ly selective noise filtration (SSNF) and NSSNF added new parameter. Methods using the spatial correlation are effectual at edge detection and image enhancement, whereas algorithm is complex and needs more computation However, UDWT is effective at denoising and needs less computation and simple algorithm.

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An Algorithm for Baseline Correction of SELDI/MALDI Mass Spectrometry Data

  • Lee, Kyeong-Eun
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.4
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    • pp.1289-1297
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    • 2006
  • Before other statistical data analysis the preprocessing steps should be performed adequately to have meaningful results. These steps include processes such as baseline correction, normalization, denoising, and multiple alignment. In this paper an algorithm for baseline correction is proposed with using the piecewise cubic Hermite interpolation with block-selected points and local minima after denoising for SELDI or MALDI mass spectrometry data.

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Nonlinear Image Denoising Algorithm in the Presence of Heavy-Tailed Noise (Heavy-tailed 잡음에 노출된 이미지에서의 비선형 잡음제거 알고리즘)

  • Hahn, Hee-Il
    • Proceedings of the KIEE Conference
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    • pp.18-20
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    • 2006
  • The statistics for the neighbor differences between the particular pixels and their neighbors are introduced. They are incorporated into the filter to remove additive Gaussian noise contaminating images. The derived denoising method corresponds to the maximum likelihood estimator for the heavy-tailed Gaussian distribution. The error norm corresponding to our estimator from the robust statistics is equivalent to Huber's minimax norm. Our estimator is also optimal in the respect of maximizing the efficacy under the above noise environment.

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PERFORMANCE OF Gℓ-PCG METHOD FOR IMAGE DENOISING PROBLEMS

  • YUN, JAE HEON
    • Journal of applied mathematics & informatics
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    • v.35 no.3_4
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    • pp.399-411
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    • 2017
  • We first provide the linear operator equations corresponding to the Tikhonov regularization image denoising problems with different regularization terms, and then we propose how to choose Kronecker product preconditioners which are required for accelerating the $G{\ell}$-PCG method. Next, we provide how to apply the $G{\ell}$-PCG method with Kronecker product preconditioner to the linear operator equations. Lastly, we provide numerical experiments for image denoisng problems to evaluate the effectiveness of the $G{\ell}$-PCG with Kronecker product preconditioner.

Denoising and Deblurring Images Using Backward Solution of Nonlinear Wave Equation

  • Lee, In-Jung;Min, Joon-Young;Lee, Hyung
    • Proceedings of the Korea Society of Information Technology Applications Conference
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    • pp.289-291
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    • 2005
  • In this paper, we introduce the backward solution of nonlinear wave equation for denoising. The PDE method is approved about 4 PSNR value compare with any convolution method. In neuro images, denoising process using proposed PDE is good about 0.2% increased Voxel Region.

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