• Title, Summary, Keyword: Denoising

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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.

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.

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.

Non-Local Means Denoising Method using Weighting Function based on Mixed norm (혼합 norm 기반의 가중치 함수를 이용한 평균 노이즈 제거 기법)

  • Kim, Dong-Young;Oh, Jong-Geun;Hong, Min-Cheol
    • Journal of IKEEE
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    • v.20 no.2
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    • pp.136-142
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    • 2016
  • This paper presents a non-local means (NLM) denoising algorithm based on a new weighting function using a mixed norm. The fidelity of the difference between an anchor patch and the reference patch in the NLM denoising depends on noise level and local activity. This paper introduces a new weighting function based on a mixed norm type of which the order is determined by noise level and local activity of an anchor patch, so that the performance of the NLM denoising can be enhanced. Experimental results demonstrate the objective and subjective capability of the proposed algorithm. In addition, it was verified that the proposed algorithm can be used to improve the performance of the other $l_2$ norm based non-local means denoising algorithms

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.

A Study on Denoising Methods using Wavelet in AWGN environment (AWGN 환경에서 웨이브렛을 이용한 잡음 제거 방법에 관한 연구)

  • 배상범;김남호
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.5 no.5
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    • pp.853-860
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    • 2001
  • This paper presents the new two denoising methods using wavelet. One is new spatially selective noise filtration(NSSNF) using spatial correlation and the other is undecimated discrete wavelet transform (UDWT) threshold-based. NSSNF got the flexible gain special property of SNR adding new parameter at the existing SSNF and UDWT had superior denosing effect than orthogonal wavelet transform(OWT) applied soft-threshold by applied hard-threshold. We selected additive white gaussian noise(AWGN) in this test environment. Also we analyzed and compared ousting denoising method using SNR as standard of judgement of improvemental effect.

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Image Denoising via Mixture Modeling of Wavelet Coefficients (웨이블릿 계수의 혼합 모델링을 이용한 영상 잡음 제거)

  • 엄일규;우동헌;김유신
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.28 no.8C
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    • pp.788-794
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    • 2003
  • It is very important to construct statistical model in order to exactly estimate the signal variance from the noisy image. By using estimated variance of original image, in general, Wiener filter is constructed, and it is applied to the noisy image. In this paper, we propose a new statistical mixture modeling of wavelet coefficients for image denoising. Firstly, a simple classification method is used to construct a significance map that captures significant property of wavelet coefficients. Based upon the significance map, the state probabilities of mixture model is computed, and signal variance is estimated by using them. Experimental results show that the proposed method yields 0.1-0.2㏈ higher PSNR than conventional methods for image denoising.

Bayesian Image Denoising with Mixed Prior Using Hypothesis-Testing Problem (가설-검증 문제를 이용한 혼합 프라이어를 가지는 베이지안 영상 잡음 제거)

  • Eom Il-Kyu;Kim Yoo-Shin
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.43 no.3
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    • pp.34-42
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    • 2006
  • In general, almost information is stored in only a few wavelet coefficients. This sparse characteristic of wavelet coefficient can be modeled by the mixture of Gaussian probability density function and point mass at zero, and denoising for this prior model is peformed by using Bayesian estimation. In this paper, we propose a method of parameter estimation for denoising using hypothesis-testing problem. Hypothesis-testing problem is applied to variance of wavelet coefficient, and $X^2$-test is used. Simulation results show our method outperforms about 0.3dB higher PSNR(peak signal-to-noise ratio) gains compared to the states-of-art denoising methods when using orthogonal wavelets.