• Title, Summary, Keyword: Denoising

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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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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 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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Auxiliary Stacked Denoising Autoencoder based Collaborative Filtering Recommendation

  • Mu, Ruihui;Zeng, Xiaoqin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.6
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    • pp.2310-2332
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    • 2020
  • In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

Image Signal Denoising by the Soft-Threshold Technique Using Coefficient Normalization in Multiwavelet Transform Domain (멀티웨이블릿 변환영역에서 계수정규화를 이용한 Soft-Threshold 기법의 영상신호 잡음제거)

  • Kim, Jae-Hwan;Woo, Chang-Yong;Park, Nam-Chun
    • Journal of the Institute of Convergence Signal Processing
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    • v.8 no.4
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    • pp.255-265
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    • 2007
  • In case of wavelet coefficients have correlation, in image signal denoising using wavelet shrinkage denoising method, the denoising effect for the image signal is reduced when the wavelet shrinkage denoising method is used. The coefficients of multiwavelet transform have correlation by pre-filters. To solve the degradation problem in multiwavelet transform, V Sterela suggested a new pre-filter for the Universal threshold or weighting factors to the threshold. In this paper, to improve the denoising effect in the multiwavelet transform, the coefficient normalizing method that the coefficient are divided by estimated noise deviation is adopted to the transformed multiwavelet coefficients in the course of wavelet shrinkage technique. And the thresholds of universal, SURE and GCV are estimated using normalized coefficients and tried to denoise by the wavelet shrinkage technique. We compared PSNRs of denoised images for each thresholds and confirmed the efficiency of the proposed method.

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Piecewise Image Denoising with Multi-scale Block Region Detector based on Quadtree Structure (쿼드트리 기반의 다중 스케일 블록 영역 검출기를 통한 구간적 영상 잡음 제거 기법)

  • Lee, Jeehyun;Jeong, Jechang
    • Journal of Broadcast Engineering
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    • v.20 no.4
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    • pp.521-532
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    • 2015
  • This paper presents a piecewise image denoising with multi-scale block region detector based on quadtree structure for effective image restoration. Proposed piecewise image denoising method suggests multi-scale block region detector (MBRD) by dividing whole pixels of a noisy image into three parts, with regional characteristics: strong variation region, weak variation region, and flat region. These regions are classified according to total pixels variation between multi-scale blocks and are applied principal component analysis with local pixel grouping, bilateral filtering, and structure-preserving image decomposition operator called relative total variation. The performance of proposed method is evaluated by Experimental results. we can observe that region detection results generated by the detector seems to be well classified along the characteristics of regions. In addition, the piecewise image denoising provides the positive gain with regard to PSNR performance. In the visual evaluation, details and edges are preserved efficiently over the each region; therefore, the proposed method effectively reduces the noise and it proves that it improves the performance of denoising by the restoration process according to the region characteristics.

Improved Nonlocal Means Algorithm for Image Denoising (영상 잡음 제거를 위해 개선된 비지역적 평균 알고리즘)

  • Park, Sang-Wook;Kang, Moon-Gi
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.1
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    • pp.46-53
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    • 2011
  • Nonlocal means denoising algorithm is one of the most widely used denoising algorithm. Because it performs well, and the theoretic idea is intuitive and simple. However the conventional nonlocal means algorithm has still some problems such as noise remaining in the denoised flat region and blurring artifacts in the denoised edge and pattern region. Thus many improved algorithms based on nonlocal means have been proposed. In this paper, we proposed new improved nonlocal means denoising algorithm by weight update through weights sorting and newly defined threshold. Updated weights can make weights more refined and definite, and denoising is possible without that artifacts. Experimental results including comparisons with conventional algorithms for various noise levels and test images show the proposed algorithm has a good performance in both visual and quantitative criteria.

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

  • Park Hyun;Moon Young-Shik
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.43 no.2
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    • pp.63-71
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    • 2006
  • Denoising and reconstruction of color images are extensively studied in the field of computer vision and image processing. Especially, 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 noise 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 extraction of facial features using active appearance model, relishing 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 denoising method maintains the structural characteristics of input faces, while efficiently removing complex color noise.

A Deep Learning-Based Face Mesh Data Denoising System (딥 러닝 기반 얼굴 메쉬 데이터 디노이징 시스템)

  • Roh, Jihyun;Im, Hyeonseung;Kim, Jongmin
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1250-1256
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    • 2019
  • Although one can easily generate real-world 3D mesh data using a 3D printer or a depth camera, the generated data inevitably includes unnecessary noise. Therefore, mesh denoising is essential to obtain intact 3D mesh data. However, conventional mathematical denoising methods require preprocessing and often eliminate some important features of the 3D mesh. To address this problem, this paper proposes a deep learning based 3D mesh denoising method. Specifically, we propose a convolution-based autoencoder model consisting of an encoder and a decoder. The convolution operation applied to the mesh data performs denoising considering the relationship between each vertex constituting the mesh data and the surrounding vertices. When the convolution is completed, a sampling operation is performed to improve the learning speed. Experimental results show that the proposed autoencoder model produces faster and higher quality denoised data than the conventional methods.