• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.97-107
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• 2001

In this paper, an illuminant estimation algorithm of a chromatic digital images proposed. The proposed illumination estimation method has two phases. First, the surface spectral reflectances are recovered. In this case, the surface spectral reflectances recovered are limited to the maximum highlight region (MHR) which is the most achromatic and highly bright region of an image after applying intermediate color constancy process using a modified gray world algorithm. Next, the surface reflectances of the maximum highlight region are estimated using the principal component analysis method along with a set of given Munsell samples. Second, the spectral distribution of reflected lights of MHR is selected from the spectral database. That is a color difference is compared between the reflected lights of the MHR and the spectral database that is the set of reflected lights built by the given Munsell samples and a set of illuminants. Then the closest colors from the spectral database are selected. Finally, the illuminant of an image can be calculated dividing the average spectral distributions of reflected lights of MHR by the average surface reflectances of the MHR. In order to evaluate the proposed algorithm, experiments with artificial and real captured color-biased scenes were performed and numerical comparison examined. The proposed method was effective in estimating the spectral of the given illuminant sunder various illuminants.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.142-149
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• 2013

In general, a color of an image is represented by using red, green, and blue channels in a RGB camera system. However, only information of three channels are limited to estimate a spectral reflectance of a real scene. Because of this, the RGB camera system can not accurately represent the color. To overcome this limitation and represent an accurate color, researches to estimate the spectral reflectance by using a multi-channel camera system are being actively proceeded. Recently, a reflectance estimation method adaptively constructing a similar training set from a traditional training set according to a camera response by using a spectral similarity was introduced. However, in this method, an accuracy of the similar training set is reduced because the spectral similarity based on an average and a maximum distances was applied. In this paper, a reflectance estimation method applied a spectral similarity based on a correlation coefficient is proposed to improve the accuracy of the similar training set. Firstly, the correlation coefficient between the similar training set and the spectral reflectance obtained by Wiener estimation method is calculated. Secondly, the similar training set is constructed from the traditional training set according to the correlation coefficient. Finally, Wiener estimation method applied the similar training set is performed to estimate the spectral reflectance. To evaluate a performance of the proposed method with previous methods, experimental results are compared. As a result, the proposed method showed the best performance.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.83-91
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• 1997

A reactor vibration monitoring has been performed using neutron noise obtained from excore detectors for the safety operation, Traditionally, the spectral estimator based on Fourier analysis has been widely used in the noise analysis of the reactor system. If the bias is too severe, the resolution would not be adequate for a given application. One major motivation for the current interests in the parametric approach to spectral estimation is the apparent higher resolution achievable with these modern techniques. In considering an unbias, a consistency, an efficency, and a minimum lower bound of the statictic estimation, an AR model is appropriate for noise spectral estimation with sharp peaks but not deep valley. In order to select an appropriate model order, the lag value of autocorrleaton function is applied. Burg method to trace the vibration mode of RPV internal is the most sucuessful.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.10
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• pp.438-444
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• 1986

The signal can be modeled as a linear combination of its past values and present and past values of a hypothetical input to system whose output is given signal. Using this model spectral estimation problem can be reduced to estimate the ARMA parameters. This paper presents recursive modified instrumental variable algorithm which can estimate AR and MA parameters. For more accurate estimation, overdetermined modified IV algorithm is also derived. Computer simulations are presented to illustrate the above methods.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.134-136
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• 1986

In this paper, a new power spectral estimation technique is presented. At first, by transforming the original data with the Karhunen-Loeve Transform(KLT), we can reduce the amount of the redundant information. Next, by modeling the transformed data by means of the autoregressive(AR) model and then applying the least-squares parameter estimation algorithm to this model, even more accurate spectrum estimates can be obtained. The KLT is the optimum transform for signal representation with respect to the mean-square error criterion. And the least-squares method is used to overcome the inherent shortcomings of popular burg algorithm.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.9-12
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• 2000

This paper proposes an improved image illumination estimation method based on the conventional color constancy algorithm. The most important process of color constancy algorithm is the estimation of the spectral distributions of illuminant of an input image. To estimate of the spectral distributions of illuminant of an input image, we use the brightest pixel values and the values of surface reflectance of an input image using a principal component analysis of the given munsell chips. We estimate a CIE tristimulus values of an input image using the estimated .spectral distribution of illuminant and recover an image by scaling it regularity. From the experimental results, the proposed method was effective in estimating the image illumination

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.11
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• pp.813-818
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• 1987

In this paper, high resolution spectral estimation by AR modelling and principal comonent analysis is proposed. The given data can be expanded by the eigenvectors of the estimated covariance matrix. The eigenspectrum is obtained for each eigenvector using the Autoressive(AR) spectral estimation technique. The final spectrum estimate is obtained by weighting each eigenspectrum with the corresponding eigenvalue and summing them. Although the proposed method increases in computational complexity, it shows good frequency resolution especially for short data records and narrow-band data whose signal-to-noise ratio is low.

• Current Optics and Photonics
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• v.4 no.6
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• pp.530-539
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• 2020

Hyperspectral images feature a relatively narrow band and are easily disturbed by noise. Accurate estimation of the types and parameters of noise in hyperspectral images can provide prior knowledge for subsequent image processing. Existing hyperspectral-noise estimation methods often pay more attention to the use of spectral information while ignoring the spatial information of hyperspectral images. To evaluate the noise in hyperspectral images more accurately, we have proposed a spectral-spatial cooperative noise-evaluation method. First, the feature of spatial information was extracted by Gabor-filter and K-means algorithms. Then, texture edges were extracted by the Otsu threshold algorithm, and homogeneous image blocks were automatically separated. After that, signal and noise values for each pixel in homogeneous blocks were split with a multiple-linear-regression model. By experiments with both simulated and real hyperspectral images, the proposed method was demonstrated to be effective and accurate, and the composition of the hyperspectral image was verified.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.3
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• pp.107-114
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• 1985

This paper presents the iterative algorithm for obtaining the ME PSE(Maximum Entropy Power Spectral Estimation) of 2-dimensional signals. This problem involves a correction matching power spectral estimate that can be represented as the reciprocal of the spectral of 2-dimensional signals. This requires two matrix inversion every iterations. Thus, we compensate the matrix to be constantly positive definite with relaxational parameters. Using Row/Column decomposition Discrete Fourier Transform, we can decrease a calculation quantity. Using Lincoln data and white noise, this paper examines ME PSE algorithms. Finally, the results output at the graphic display device. The 2-dimensional data have the 3-dimensional axis components, and, this paper develops 3-dimensional graphic output algorithms using 2-dimensional DGL(Device Independent Graphic Library) which is prepared for HP-1000 F-series computer.

• Journal of the Optical Society of Korea
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• v.15 no.1
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• pp.103-108
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• 2011

Recently reported asynchronous-optical-sampling terahertz (THz) time-domain spectroscopy enables high-resolution spectroscopy due to a long time-delay window. However, a long-lasting tail signal following the main pulse is often measured in a time-domain waveform, resulting in spectral fluctuation above a background noise level on a high-resolution THz amplitude spectrum. Here, we adopt the wavelet power spectrum estimation technique (WPSET) to effectively remove the spectral fluctuation without sacrificing spectral features. Effectiveness of the WPSET is verified by investigating a transmission spectrum of water vapor.