• Title, Summary, Keyword: Denoising

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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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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.

Partial Discharge Signal Denoising using Adaptive Translation Invariant Wavelet Transform-Online Measurement

  • Maheswari, R.V.;Subburaj, P.;Vigneshwaran, B.;Iruthayarajan, M. Willjuice
    • Journal of Electrical Engineering and Technology
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    • v.9 no.2
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    • pp.695-706
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    • 2014
  • Partial discharge (PD) measurements have emerged as a dominant investigative tool for condition monitoring of insulation in high voltage equipment. But the major problem behind them the PD signal is severely polluted by several noises like White noise, Random noise, Discrete Spectral Interferences (DSI) and the challenge lies with removing these noise from the onsite PD data effectively which leads to preserving the signal for feature extraction. Accordingly the paper is mainly classified into two parts. In first part the PD signal is artificially simulated and mixed with white noise. In second part the PD is measured then it is subjected to the proposed denoising techniques namely Translation Invariant Wavelet Transform (TIWT). The proposed TIWT method remains the edge of the original signal efficiently. Additionally TIWT based denoising is used to suppress Pseudo Gibbs phenomenon. In this paper an attempt has been made to review the methodology of denoising the PD signals and shows that the proposed denoising method results are better when compared to other wavelet-based approaches like Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), by evaluating five different parameters like, Signal to noise ratio, Cross-correlation coefficient, Pulse amplitude distortion, Mean square error, Reduction in noise level.

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.

Acceleration of Mesh Denoising Using GPU Parallel Processing (GPU의 병렬 처리 기능을 이용한 메쉬 평탄화 가속 방법)

  • Lee, Sang-Gil;Shin, Byeong-Seok
    • Journal of Korea Game Society
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    • v.9 no.2
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    • pp.135-142
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    • 2009
  • Mesh denoising is a method to remove noise applying various filters. However, those methods usually spend much time since filtering is performed on CPU. Because GPU is specialized for floating point operations and faster than CPU, real-time processing for complex operations is possible. Especially mesh denoising is adequate for GPU parallel processing since it repeats the same operations for vertices or triangles. In this paper, we propose mesh denoising algorithm based on bilateral filtering using GPU parallel processing to reduce processing time. It finds neighbor triangles of each vertex for applying bilateral filter, and computes its normal vector. Then it performs bilateral filtering to estimate new vertex position and to update its normal vector.

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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.

Denoising ISTA-Net: learning based compressive sensing with reinforced non-linearity for side scan sonar image denoising (Denoising ISTA-Net: 측면주사 소나 영상 잡음제거를 위한 강화된 비선형성 학습 기반 압축 센싱)

  • Lee, Bokyeung;Ku, Bonwha;Kim, Wan-Jin;Kim, Seongil;Ko, Hanseok
    • The Journal of the Acoustical Society of Korea
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    • v.39 no.4
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    • pp.246-254
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    • 2020
  • In this paper, we propose a learning based compressive sensing algorithm for the purpose of side scan sonar image denoising. The proposed method is based on Iterative Shrinkage and Thresholding Algorithm (ISTA) framework and incorporates a powerful strategy that reinforces the non-linearity of deep learning network for improved performance. The proposed method consists of three essential modules. The first module consists of a non-linear transform for input and initialization while the second module contains the ISTA block that maps the input features to sparse space and performs inverse transform. The third module is to transform from non-linear feature space to pixel space. Superiority in noise removal and memory efficiency of the proposed method is verified through various experiments.

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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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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