Wiener estimation method in estimating of spectral reflectance from RGB images

Stigell, P.; Miyata, Kimiyoshi; Hauta-Kasari, Markku

doi:10.1134/s1054661807020101

Cited by 77 publications

(69 citation statements)

References 2 publications

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“…(12). The root-mean-square error (RMSE) [16] is used to evaluate the accuracy of spectral reflectance reconstruction, where the original spectral reflectance of each pixel is r i , the reconstructed spectral reflectance of each pixel is    , and the RMSE is as in Eq. (13):…”

Section: Simulationsmentioning

confidence: 99%

Study of Spectral Reflectance Reconstruction Based on an Algorithm for Improved Orthogonal Matching Pursuit

Zhang¹,

Liang²,

Zhang³

et al. 2016

Journal of the Optical Society of Korea

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Spectral reflectance is sparse in space, and while the traditional spectral-reconstruction algorithm does not make full use of this characteristic sparseness, the compressive sensing algorithm can make full use of it. In this paper, on the basis of analyzing compressive sensing based on the orthogonal matching pursuit algorithm, a new algorithm based on the Dice matching criterion is proposed. The Dice similarity coefficient is introduced, to calculate the correlation coefficient of the atoms and the residual error, and is used to select the atoms from a library. The accuracy of Spectral reconstruction based on the pseudo-inverse method, Wiener estimation method, OMP algorithm, and DOMP algorithm is compared by simulation on the MATLAB platform and experimental testing. The result is that spectral-reconstruction accuracy based on the DOMP algorithm is higher than for the other three methods. The root-mean-square error and color difference decreases with an increasing number of principal components. The reconstruction error decreases as the number of iterations increases. Spectral reconstruction based on the DOMP algorithm can improve the accuracy of color-information replication effectively, and high-accuracy color-information reproduction can be realized.

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Section: Simulationsmentioning

confidence: 99%

Study of Spectral Reflectance Reconstruction Based on an Algorithm for Improved Orthogonal Matching Pursuit

Zhang¹,

Liang²,

Zhang³

et al. 2016

Journal of the Optical Society of Korea

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“…Also, E and S are k × k diagonal matrices representing the spectrum of the illuminant and the sensitivity of the camera, respectively. The Wiener estimation [33][34][35] of r is given byr…”

Section: Estimation Of Spectral Diffuse Reflectance Images By Wiener mentioning

confidence: 99%

“…On the other hand, the reconstruction of multispectral images from a red green blue (RGB) image acquired by a digital RGB camera is promising as a method of rapid and cost-effective multispectral imaging. Several reconstruction techniques for multispectral images, such as the pseudo-inverse method, [27][28][29][30] finite-dimensional modeling, 29,31 the nonlinear estimation method, 32 and the Wiener estimation method (WEM), [33][34][35] have been investigated. Among these reconstruction techniques, the WEM is one of the most promising methods for practical applications because of its simplicity, cost-effectiveness, accuracy, time efficiency, and the possibility of high-resolution image acquisition.…”

Section: Introductionmentioning

confidence: 99%

Multispectral imaging of absorption and scattering properties ofin vivoexposed rat brain using a digital red-green-blue camera

et al. 2015

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Abstract. In order to estimate multispectral images of the absorption and scattering properties in the cerebral cortex of in vivo rat brain, we investigated spectral reflectance images estimated by the Wiener estimation method using a digital RGB camera. A Monte Carlo simulation-based multiple regression analysis for the corresponding spectral absorbance images at nine wavelengths (500, 520, 540, 560, 570, 580, 600, 730, and 760 nm) was then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentrations of oxygenated hemoglobin and that of deoxygenated hemoglobin were estimated as the absorption parameters, whereas the coefficient a and the exponent b of the reduced scattering coefficient spectrum approximated by a power law function were estimated as the scattering parameters. The spectra of absorption and reduced scattering coefficients were reconstructed from the absorption and scattering parameters, and the spectral images of absorption and reduced scattering coefficients were then estimated. In order to confirm the feasibility of this method, we performed in vivo experiments on exposed rat brain. The estimated images of the absorption coefficients were dominated by the spectral characteristics of hemoglobin. The estimated spectral images of the reduced scattering coefficients had a broad scattering spectrum, exhibiting a larger magnitude at shorter wavelengths, corresponding to the typical spectrum of brain tissue published in the literature. The changes in the estimated absorption and scattering parameters during normoxia, hyperoxia, and anoxia indicate the potential applicability of the method by which to evaluate the pathophysiological conditions of in vivo brain due to the loss of tissue viability. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…There are several solutions available in the literature. [6][7][8][9][10][11][12] In this paper, polynomial multivariate leastsquares regression (PMLR) was used to estimate the transform matrix T and thus the hyperspectral data. The polynomial regression model assumes that there is a nonlinear relationship between the predictors (color image) and the dependent variables (hyperspectral image).…”

Section: Reconstruction Of Hyperspectral Datamentioning

confidence: 99%

“…6,7 Some of the hyperspectral estimation techniques to recover reflectance spectra include Wiener estimation, multiple regression analysis, Maloney-Wandell method, Imai-Berns method, and Shi-Healey method. [6][7][8][9][10][11][12] Hyperspectral images have also been used to develop a hyperspectral estimation method from color and/or multispectral images for spectral estimation of paint. 8 A different group of techniques for hyperspectral estimation is based on sparse signal representation and sampling, such as compressive sensing.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging using RGB color for foodborne pathogen detection

Yoon

Shin

Lawrence

et al. 2015

J. Electron. Imaging

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Abstract. This paper reports the development of a spectral reconstruction technique for predicting hyperspectral images from RGB color images and classifying food-borne pathogens in agar plates using reconstructed hyperspectral images. The six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown on Rainbow agar plates were used for the study. A line-scan pushbroom hyperspectral imaging spectrometer was used to scan full reflectance spectra of pure non-O157 STEC cultures in the visible and near-infrared spectral range from 400 to 1000 nm. RGB color images were generated by simulation from hyperspectral images. Polynomial multivariate least-squares regression analysis was used to reconstruct hyperspectral images from RGB color images. The mean R-squared value for hyperspectral image reconstruction was ∼0.98 in the spectral range between 400 and 700 nm for linear, quadratic, and cubic polynomial regression models. The accuracy of the hyperspectral image classification algorithm based on k-nearest neighbors algorithm of principal component scores was validated to be 92% with the test set (99% with the original hyperspectral images). The results of the study suggested that color-based hyperspectral imaging would be feasible without much loss of prediction accuracy compared to true hyperspectral imaging. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Wiener estimation method in estimating of spectral reflectance from RGB images

Cited by 77 publications

References 2 publications

Study of Spectral Reflectance Reconstruction Based on an Algorithm for Improved Orthogonal Matching Pursuit

Study of Spectral Reflectance Reconstruction Based on an Algorithm for Improved Orthogonal Matching Pursuit

Multispectral imaging of absorption and scattering properties ofin vivoexposed rat brain using a digital red-green-blue camera

Hyperspectral imaging using RGB color for foodborne pathogen detection

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