• Journal of KIISE
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• v.41 no.11
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• pp.943-957
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• 2014

Removing noise, called denoising, is an essential factor for the more intuitive and more accurate results in boundary image matching. This paper deals with a partial denoising problem that tries to allow a limited amount of partial noise embedded in boundary images. To solve this problem, we first define partial denoising time-series which can be generated from an original image time-series by removing a variety of partial noises and propose an efficient mechanism that quickly obtains those partial denoising time-series in the time-series domain rather than the image domain. We next present the partial denoising distance, which is the minimum distance from a query time-series to all possible partial denoising time-series generated from a data time-series, and we use this partial denoising distance as a similarity measure in boundary image matching. Using the partial denoising distance, however, incurs a severe computational overhead since there are a large number of partial denoising time-series to be considered. To solve this problem, we derive a tight lower bound for the partial denoising distance and formally prove its correctness. We also propose range and k-NN search algorithms exploiting the partial denoising distance in boundary image matching. Through extensive experiments, we finally show that our lower bound-based approach improves search performance by up to an order of magnitude in partial denoising-based boundary image matching.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.35-40
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• 2019

In this paper, we design and implement a partial denoising boundary matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform a fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI(graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client and sends the resulting images to the client. Experimental results show that our system provides many intuitive and accurate matching results.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1343-1350
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• 2019

Converting boundary images to time-series makes it feasible to perform boundary matching even on a very large image database, which is very important for interactive and fast matching. In recent research, there has been an attempt to perform fast matching considering partial denoising by converting the boundary image into time series. In this paper, to improve performance, we propose an index-building method considering all possible arbitrary denoising parameters for removing arbitrary partial noises. This is a challenging problem since the partial denoising boundary matching must be considered for all possible denoising parameters. We propose an efficient single index-building algorithm by constructing a minimum bounding rectangle(MBR) according to all possible denoising parameters. The results of extensive experiments conducted show that our index-based matching method improves the search performance up to 46.6 ~ 4023.6 times.

• 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.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.335-341
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• 2009

Analysis of the partial discharge signal, a technique to diagnose the stator winding insulation is a key function for the diagnosis of high voltage rotating machines and requires high precision. To satisfy this requirement, various denoising techniques such as filtering and differential methods were proposed. However, these techniques can not eliminate a adjacent induction noise that decreases reliability of the diagnosis. A simple novel denoising algorithm, therefore, is proposed for removing the adjacent induction noise in this paper. The algorithm shows good performance in the real partial discharge signals measured by 13kV class capacitive couplers installed at hydro-generator in Dae-cheong Dam.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.436-450
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• 2017

Recently, the method based on fractional order partial differential equation has been used in image processing. Usually, the optional order of fractional differentiation is determined by a lot of experiments. In this paper, a denoising model is proposed based on adaptive fractional order anisotropic diffusion. In the proposed model, the complexity of the local image texture is reflected by the local variance, and the order of the fractional differentiation is determined adaptively. In the process of the adaptive fractional order model, the discrete Fourier transform is applied to compute the fractional order difference as well as the dynamic evolution process. Experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) of the proposed image denoising algorithm is better than that of other some algorithms. The proposed algorithm not only can keep the detailed image information and edge information, but also obtain a good visual effect.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.4
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• pp.62-68
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• 2001

In this paper, As the wavelet transform has the properties of multi-resolution analysis and time-frequency domain localization, application of wavelet transform is used at partial discharge(PD) signal detected by electrical detection method to extract PD signal's various frequency component and its time domain. therefore we can analyzed PD signal's time-frequency domain simultaneously. On the other hand, using wavelet transform denoising process, included noise signal in detected PD signal is well eliminated. we can propose the true shine of PD signal.

• Proceedings of the IEEK Conference
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• 2005.11a
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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.