• Title, Summary, Keyword: Denoising

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Feasibility Study of Improved Patch Group Prior Based Denoising (PGPD) Technique with Medical Ultrasound Imaging System

  • Kim, Seung Hun;Seo, Kanghyen;Kang, Seong Hyeon;Kim, Jong Hun;Choi, Won Ho;Lee, Youngjin
    • Journal of Magnetics
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    • v.22 no.1
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    • pp.55-59
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    • 2017
  • The purpose of this study was to quantitatively evaluate image quality using intensity profile, coefficient of variation (COV), and peak signal to noise ratio (PSNR) with respect to noise reduction techniques in the ultrasound images. For that purpose, we compared with the median filter, Rudin-Osher-Fatemi (ROF), Anscombe and proposed patch group prior based denoising (PGPD) techniques. To evaluate image quality, the Shepp-Logan phantom and the ultrasound image were acquired using simulation and experiment, respectively. According to the results, the difference of intensity profile using PGPD technique is lowest compared with original Shepp-Logan phantom. In simulation, the measured COV was 0.249, 0.198, 0.198, 0.177, and 0.080 using noisy, median, ROF, Anscombe and PGPD technique, respectively. Also, in experimental image, the measured COV was 0.245, 0.230, 0.231, 0.242 and 0.187 using noisy, median, ROF, Anscombe and PGPD technique, respectively. Especially, when we used PGPD technique, the PSNR has highest value in both simulation and experiment. In this study, we performed simulation and experiment study to compare various denoising techniques in the ultrasound image. We can expect the PGPD technique to improve in medical diagnosis with excellent noise reduction.

Soft Thresholding Method Using Gabor Cosine and Sine Transform for Image Denoising (영상 잡음제거를 위한 게이버 코사인과 사인 변환의 소프트 문턱 방법)

  • Lee, Juck-Sik
    • Journal of the Institute of Convergence Signal Processing
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    • v.11 no.1
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    • pp.1-8
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    • 2010
  • Noise removal methods for noisy images have been studied a lot in the domain of spatial and transform filtering. Low pass filtering was initially applied in the spatial domain. Recently, discrete wavelet transform has widely used for image denoising as well as image compression due to an excellent energy compaction and a property of multiresolution. In this paper, Gabor cosine and sine transform which is considered as human visual filter is applied to image denoising areas using soft thresholding technique. GCST is compared with excellent wavelet transform which uses existing soft thresholding methods from PSNR point of view. Resultant images removed noises are also visually compared. Experimental results with adding four different standard deviation levels of Gaussian distributed noises to real images show that the proposed transform has better PSNR performance of a maximum of 1.18 dB and visible perception than wavelet transform.

POCS Based Interpolation Method for Irregularly Sampled Image (불규칙한 샘플 영상에 대한 POCS 기반 보간법)

  • Lee, Jong-Hwa;Lee, Chul-Hee
    • Journal of Broadcast Engineering
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    • v.16 no.4
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    • pp.669-679
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    • 2011
  • In this paper, we propose a POCS based irregularly sampled image interpolation method exploiting non-local block-based wavelet shrinkage denoising algorithm. The method provides convex sets to improve the performance. The Delaunay triangulation interpolation is first applied to interpolate the missing pixels of the irregularly sampled image into the regular grids. Then, the non-local block-based wavelet shrinkage denoising algorithm is applied, and the originally observed pixels are enforced. After iteration is performed, the denoising algorithm for non-edge areas is applied to acquire the final result. The experimental results show that the proposed method outperforms the conventional methods.

A Study on Hybrid Filter Algorithm for Image Denoising (영상 잡음제거를 위한 하이브리드 필터 알고리즘에 관한 연구)

  • Yinyu, Gao;Kim, Nam-Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.127-129
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    • 2012
  • Due to the prevalence of digital camera, multi-media etc. the image is being used in everyday life. However, noise always damages the image and the image denoising technology is important part for improving the image visual quality. There are many existing methods to remove noise such as wiener filter, mean filter and VisuShrink etc. However, they perform not good enough for denoising. Hence, in this paper we proposed a hybrid filter algorithm which consists of wiener filter and modified wavelet based thresholding method using adaptive threshold and thresholding function. The proposed algorithm shows not only better low frequency and high frequency property, but also the outstanding noise suppression and edge preservation properties.

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Noise Reduction Using Gaussian Mixture Model and Morphological Filter (가우스 혼합모델과 형태학적 필터를 이용한 잡음 제거)

  • Eom Il-Kyu;Kim Yoo-Shin
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.1
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    • pp.29-36
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    • 2004
  • Generally, wavelet coefficients can be classified into two categories: large coefficients with much signal information and small coefficients with little signal component. This statistical characteristic of wavelet coefficient is approximated to Gaussian mixture model and efficiently applied to noise reduction. In this paper, we propose an image denoising method using mixture modeling of wavelet coefficients. Binary mask value is generated by proper threshold which classifies wavelet coefficients into two categories. Information of binary mask value is used to remove image noise. We also develope an enhancement method of mask value using morphological filter, and apply it to image denoising for improvement of the proposed method. Simulation results shows the proposed method have better PSNRs than those of the state of art denoising methods.

A Complex Noise Suppression Algorithm for On-line Partial Discharge Diagnosis Systems (운전중 부분방전 진단시스템을 위한 복합 잡음제거 기법)

  • Yi, Sang-Hwa;Youn, Young-Woo;Choo, Young-Bae;Kang, Dong-Sik
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.2
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    • pp.342-348
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    • 2009
  • This paper introduces a novel denoising algorithm for the partial-discharge(PD) signals from power apparatuses. The developed algorithm includes three kinds of specific denoising sub-algorithms. The first sub-algorithm uses the fuzzy logic which classifies the noise types in the magnitude versus phase PD pattern. This sub-algorithm is especially effective in the rejection of the noise with high and constant magnitude. The second one is the method simply removing the pulses in the phase sections below the threshold count in the count versus phase pattern. This method is effective in removing the occasional high level noise pulses. The last denoising sub-algorithm uses the grouping characteristics of PD pulses in the 3D plot of the magnitude versus phase versus cycle. This special technique can remove the periodical noise pulses with varying magnitudes, which are very difficult to be removed by other denoising methods. Each of the sub-algorithm has different characteristic and shows different quality of the noise rejection. On that account, a parameter which numerically expresses the noise possessing degree of signal, is defined and evaluated. Using the parameter and above three sub-algorithms, an adaptive complex noise rejection algorithm for the on-line PD diagnosis system is developed. Proposed algorithm shows good performances in the various real PD signals measured from the power apparatuses in the Korean plants.

High-frame-rate Video Denoising for Ultra-low Illumination

  • Tan, Xin;Liu, Yu;Zhang, Zheng;Zhang, Maojun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.11
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    • pp.4170-4188
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    • 2014
  • In this study, we present a denoising algorithm for high-frame-rate videos in an ultra-low illumination environment on the basis of Kalman filtering model and a new motion segmentation scheme. The Kalman filter removes temporal noise from signals by propagating error covariance statistics. Regarded as the process noise for imaging, motion is important in Kalman filtering. We propose a new motion estimation scheme that is suitable for serious noise. This scheme employs the small motion vector characteristic of high-frame-rate videos. Small changing patches are intentionally neglected because distinguishing details from large-scale noise is difficult and unimportant. Finally, a spatial bilateral filter is used to improve denoising capability in the motion area. Experiments are performed on videos with both synthetic and real noises. Results show that the proposed algorithm outperforms other state-of-the-art methods in both peak signal-to-noise ratio objective evaluation and visual quality.

ECG Denoising by Modeling Wavelet Sub-Band Coefficients using Kernel Density Estimation

  • Ardhapurkar, Shubhada;Manthalkar, Ramchandra;Gajre, Suhas
    • Journal of Information Processing Systems
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    • v.8 no.4
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    • pp.669-684
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    • 2012
  • Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.

A Study on Improved Denoising Algorithm for Edge Preservation in AWGN Environments (AWGN환경에서 에지보호를 위한 개선된 잡음제거 알고리즘에 관한 연구)

  • Yinyu, Gao;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.16 no.8
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    • pp.1773-1778
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    • 2012
  • Nowadays, the high quality of image is required with the demand for digital image processing devices is rapidly increasing. But image always damaged by many kinds of noises and it is necessary to remove noise and the denoising becomes one of the most important fields. In many cases image is corrupted by AWGN(additive white Gaussian noise). In this paper, we proposed an improved denoising algorithm with edge preservation. The proposed algorithm averages values processed by spatial weighted filter and self adaptive weighted filter. Then we add the value which is computed by the equation considering variance of mask and the estimated noise variance. Through the experience, the proposed filter performs well on noise suppression and edge preservation properties and improves the image visual quality.

Noisy Power Quality Recognition System using Wavelet based Denoising and Neural Networks (웨이블릿 기반 잡음제거와 신경회로망을 이용한 잡음 전력 품질 인식 시스템)

  • Chong, Won-Yong
    • Journal of the Institute of Convergence Signal Processing
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    • v.13 no.2
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    • pp.91-98
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    • 2012
  • Power Quality (PQ) signal such as sag, swell, harmonics, and impulsive transients are the major issues in the operations of the power electronics based devices and microprocessor based equipments. The effectiveness of wavelet based denoising techniques and recognizing different power quality events with noise has been presented in this paper. The algorithms involved in the noisy PQ recognition system are the wavelet based denoising and the back propagation neural networks. Also, in order to verify the real-time performances of the noisy PQ recognition systems under the noisy environments, SIL(Software In the Loop) and PIL(Processor In the Loop) were carried out, resulting in the excellent recognition performances.