• Title, Summary, Keyword: Denoising

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Denoising Algorithm using Wavelet (웨이브렛을 이용한 잡음 제거 알고리즘)

  • 배상범;김남호
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.8
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    • pp.1139-1145
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    • 2002
  • Wavelet transformed data can filter signal with each frequency band, because it includes detail information about original signal. Therefore, in this paper, important two noises were removed by wavelet. About AWGN environment UDWT(undecimated discrete wavelet transform), applying hard-threshold, was used and about impulse noise environment, it can be possible to recognize edge of original signal as well as superior denoising effect by using two methods, denoising by threshold and slope of signal by wavelet. SNR was used as a judgemental criterion of a denoising effect and Blocks and DTMF(dual tone multi frequency) were used as a test signal.

A Study on Denoising Method using Wavelet in Impulse Noise Environment (임펄스 노이즈 환경에서 웨이브렛을 이용한 노이즈 제거 방법에 관한 연구)

  • 배상범;김남호
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.4
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    • pp.513-518
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    • 2002
  • This paper presents the new method for removing impulse noise using wavelet. Time and frequency-localization capabilities in denoising can provide excellent specialties comparing with the existing methods, because of including detail information of signal. The method in this paper, using denoising by threshold and slope of signal by wavelet, has superior denoising effect and can recognize edge of original signal. For objective judgement, the test signals are used HeaviSine and DTMF and this paper simulated by test signals which have added to impulse noise of different site individually.

A Study on Real-time Data Acquisition System and Denoising for Energy Saving Device (에너지 절약 장치용 실시간 데이터 획득 시스템 구현과 잡음제거에 관한 연구)

  • Huh, Keol;Choi, Yong-Kil;Jeong, Won-Kyo;Hoang, Chan-Ku
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • pp.47-53
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    • 2004
  • The paper shows that the combination of the hardware, NI PCI 6110E board and the software, Fourier and continuous wavelet transform(CWT) can be used to implement for extracting the important features of the real-time signal. The results confirmed that CWT produces the fast computation enough for the application of the real-time signal processing except the negligible time delay. In denoising case, because of the lack of translation invariance of wavelet basis, traditional wavelet thresholding leads to pseudo-Gibbs phenomena in the vicinity of discontinuities of signal. In this paper, in order to reduce the pseudo-Gibbs phenomena, wavelet coefficients are threshold and reconstruction algorithm is implement through shift-invariant gibbs free denoising algorithm based on wavelet transform footprint. The proposed algorithm can potentially be extended to more general signals like piecewise smooth signals and represents an effective solution to problems like signal denoising.

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Real-Time Non-Local Means Image Denoising Algorithm Based on Local Binary Descriptor

  • Yu, Hancheng;Li, Aiting
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.2
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    • pp.825-836
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    • 2016
  • In this paper, a speed-up technique for the non-local means (NLM) image denoising method based on local binary descriptor (LBD) is proposed. In the NLM, most of the computation time is spent on searching for non-local similar patches in the search window. The local binary descriptor which represents the structure of patch as binary strings is employed to speed up the search process in the NLM. The descriptor allows for a fast and accurate preselection of non-local similar patches by bitwise operations. Using this approach, a tradeoff between time-saving and noise removal can be obtained. Simulations exhibit that despite being principally constructed for speed, the proposed algorithm outperforms in terms of denoising quality as well. Furthermore, a parallel implementation on GPU brings NLM-LBD to real-time image denoising.

Image Denoising of Human Visual Filter Using GCST (GCST를 이용한 인간시각필터의 영상 잡음 제거)

  • Lee, Juck-Sik
    • Journal of the Institute of Convergence Signal Processing
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    • v.9 no.4
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    • pp.253-260
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    • 2008
  • Image denoising as one of image enhancement methods has been studied a lot in the spatial and transform domain filtering. Recently wavelet transform which has an excellent energy compaction and a property of multiresolution has widely used for image denoising. But a transform based on human visual system is visually useful if an end user is human beings. Therefore, Gabor cosine and sine transform which is considered as human visual filter is applied to image denoising areas in this paper. Denoising performance of the proposed transform is compared with those of the derivatives of Gaussian transform being another human visual filter and of discrete wavelet transform in terms of PSNR. With three levels of various noises, experimental results for real images show that the proposed transform has better PSNR performance of 0.41dB than DWT and 0.14dB than DGT.

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A study on non-local image denoising method based on noise estimation (노이즈 수준 추정에 기반한 비지역적 영상 디노이징 방법 연구)

  • Lim, Jae Sung
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.5
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    • pp.518-523
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    • 2017
  • This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.

Index-based Boundary Matching Supporting Partial Denoising for Large Image Databases

  • Kim, Bum-Soo
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.10
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    • pp.91-99
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    • 2019
  • In this paper, we propose partial denoising boundary matching based on an index for faster matching in very large image databases. Attempts have recently been made to convert boundary images to time-series with the objective of solving the partial denoising problem in boundary matching. In this paper, we deal with the disk I/O overhead problem of boundary matching to support partial denoising in a large image database. Although the solution to the problem superficially appears trivial as it only applies indexing techniques to boundary matching, it is not trivial since multiple indexes are required for every possible denoising parameters. Our solution is an efficient index-based approach to partial denoising using $R^*-tree$ in boundary matching. The results of experiments conducted show that our index-based matching methods improve search performance by orders of magnitude.

Efficient Image Denoising Method Using Non-local Means Method in the Transform Domain (변환 영역에서 Non-local Means 방법을 이용한 효율적인 영상 잡음 제거 기법)

  • Kim, Dong Min;Lee, Chang Woo
    • Journal of the Institute of Electronics and Information Engineers
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    • v.53 no.10
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    • pp.69-76
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    • 2016
  • In this paper, an efficient image denoising method using non-local means (NL-means) method in the transform domain is proposed. Survey for various image denoising methods has been given, and the performances of the image denoising method using NL-means method have been analyzed. We propose an efficient implementation method for NL-means method by calculating the weights for NL-means method in the DCT and LiftLT transform domain. By using the proposed method, the computational complexity is reduced, and the image denoising performance improves by using the characteristics of images in the tranform domain efficiently. Moreover, the proposed method can be applied efficiently for performing image denoising and image rescaling simultaneously. Extensive computer simulations show that the proposed method shows superior performance to the conventional methods.

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.

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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