• Title, Summary, Keyword: Denoising

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Implementation of Deep CNN denoiser for Reducing Over blur (Over blur를 감소시킨 Deep CNN 구현)

  • Lee, Sung-Hun;Lee, Kwang-Yeob;Jung, Jun-Mo
    • Journal of IKEEE
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    • v.22 no.4
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    • pp.1242-1245
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    • 2018
  • In this paper, we have implemented a network that overcomes the over-blurring phenomenon that occurs when removing Gaussian noise. In the conventional filtering method, blurring of the original image is performed to remove noise, thereby eliminating high frequency components such as edges and corners. We propose a network that reducing over blurring while maintaining denoising performance by adding denoised high frequency components to denoisers based on CNN.

A New Method for Selecting Thresholding on Wavelet Packet Denoising for Speech Enhancement

  • Kim, I-jae;Kim, Hyoung-soo;Koh, Kwang-hyun;Yang, Sung-il;Y. Kwon
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.2E
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    • pp.25-29
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    • 2001
  • In this paper, we propose a new method for selecting the threshold on wavelet packet denoising. In selecting threshold, the method using median is not efficient. Because this method can not recover unvoiced signal corrupted by noise. So we partition a speech signal corrupted by noise into the pure noise section and voiced section using autocorrelation and entropy. The autocorrelation and entropy can reflect disorder of noise. The new method yields more improved denoising effect. Especially unvoiced signal is very nicely reconstructed, and SNR is improved.

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A Neuro-Fuzzy Inference System for Sensor Failure Detection Using Wavelet Denoising, PCA and SPRT

  • Na, Man-Gyun
    • Nuclear Engineering and Technology
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    • v.33 no.5
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    • pp.483-497
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    • 2001
  • In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system The PCA is used to reduce the dimension of an input space without losing a significant amount of information. The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors.

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Improvement of a Low Cost MEMS Inertial-GPS Integrated System Using Wavelet Denoising Techniques

  • Kang, Chang-Ho;Kim, Sun-Young;Park, Chan-Gook
    • International Journal of Aeronautical and Space Sciences
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    • v.12 no.4
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    • pp.371-378
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    • 2011
  • In this paper, the wavelet denoising techniques using thresholding method are applied to the low cost micro electromechanical system (MEMS)-global positioning system(GPS) integrated system. This was done to improve the navigation performance. The low cost MEMS signals can be distorted with conventional pre-filtering method such as low-pass filtering method. However, wavelet denoising techniques using thresholding method do not distort the rapidly-changing signals. They can reduce the signal noise. This paper verified the improvement of the navigation performance compared to the conventional pre-filtering by simulation and experiment.

A Study on Wavelet-based Denoising Algorithm for Signal Reconstruction in Mixed Noise Environments

  • Bae, Sang-Bum;Kim, Nam-Ho
    • Journal of information and communication convergence engineering
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    • v.5 no.1
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    • pp.1-6
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    • 2007
  • In the process of the acquisition, storage, transmission of signals, noises are generated by various causes and the degradation phenomenon by noises tends to generate serious errors for the signal with information. So, in order to analyze and remove these noises, studies on numerous mathematical methods such as the Fourier transform have been implemented. And recently there have been many ongoing wavelet-based denoising algorithms representing excellent characteristics in time-frequency localization and multiresolution analysis, but the method to remove additive white Gaussian noise (AWGN) and the impulse noise simultaneously was not given. So, to reconstruct the corrupted signal by noises, in this paper a novel wavelet-based denoising algorithm was proposed and using signal-to-noise ratio (SNR) this method was compared to conventional methods.

Wavelet Denoising Using Region Merging (영역 병합을 이용한 웨이블릿 잡음 제거)

  • Eom Il kyu;Kim Yoo shin
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.30 no.3C
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    • pp.119-124
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    • 2005
  • In this paper, we propose a novel algorithm for determining the variable size of locally adaptive window using region-merging method. A region including a denoising point is partitioned to disjoint sub-regions. Locally adaptive window for denoising is obtained by selecting Proper sub-lesions. In our method, nearly arbitrarily shaped window is achieved. Experimental results show that our method outperforms other critically sampled wavelet denoising scheme.

Automatic Denoising in 2D Color Face Images Using Recursive PCA Reconstruction (2D 칼라 얼굴 영상에서 반복적인 PCA 재구성을 이용한 자동적인 잡음 제거)

  • Park, Hyun;Moon, Young-Shik
    • Proceedings of the IEEK Conference
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    • pp.1157-1160
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    • 2005
  • The denoising and reconstruction of color images are increasingly studied in the field of computer vision and image processing. Especially, the denoising and reconstruction of color face images are more difficult than those of natural images because of the structural characteristics of human faces as well as the subtleties of color interactions. In this paper, we propose a denoising method based on PCA reconstruction for removing complex color noises on human faces, which is not easy to remove by using vectorial color filters. The proposed method is composed of the following five steps; training of canonical eigenface space using PCA, automatic extracting of face features using active appearance model, relighing of reconstructed color image using bilateral filter, extraction of noise regions using the variance of training data, and reconstruction using partial information of input images (except the noise regions) and blending of the reconstructed image with the original image. Experimental results show that the proposed denosing method efficiently removes complex color noises on input face images.

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A Kalman Filter based Video Denoising Method Using Intensity and Structure Tensor

  • Liu, Yu;Zuo, Chenlin;Tan, Xin;Xiao, Huaxin;Zhang, Maojun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.8
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    • pp.2866-2880
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    • 2014
  • We propose a video denoising method based on Kalman filter to reduce the noise in video sequences. Firstly, with the strong spatiotemporal correlations of neighboring frames, motion estimation is performed on video frames consisting of previous denoised frames and current noisy frame based on intensity and structure tensor. The current noisy frame is processed in temporal domain by using motion estimation result as the parameter in the Kalman filter, while it is also processed in spatial domain using the Wiener filter. Finally, by weighting the denoised frames from the Kalman and the Wiener filtering, a satisfactory result can be obtained. Experimental results show that the performance of our proposed method is competitive when compared with state-of-the-art video denoising algorithms based on both peak signal-to-noise-ratio and structural similarity evaluations.

DWT-based Denoising and Power Quality Disturbance Detection

  • Ramzan, Muhammad;Choe, Sangho
    • IEIE Transactions on Smart Processing and Computing
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    • v.4 no.5
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    • pp.330-339
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    • 2015
  • Power quality (PQ) problems are becoming a big issue, since delicate complex electronic devices are widely used. We present a new denoising technique using discrete wavelet transform (DWT), where a modified correlation thresholding is used in order to reliably detect the PQ disturbances. We consider various PQ disturbances on the basis of IEEE-1159 standard over noisy environments, including voltage swell, voltage sag, transient, harmonics, interrupt, and their combinations. These event signals are decomposed using DWT for the detection of disturbances. We then evaluate the PQ disturbance detection ratio of the proposed denoising scheme over Gaussian noise channels. Simulation results also show that the proposed scheme has an improved signal-to-noise ratio (SNR) over existing scheme.