Characterization of trichromatic color cameras by using a new multispectral imaging technique

Cheung, Vien; Westland, Stephen; Li, Changjun; Hardeberg, Jon Yngve; Connah, David

doi:10.1364/josaa.22.001231

Cited by 96 publications

(87 citation statements)

References 15 publications

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“…For comparison, the direct-mapping method was also applied to a standard set of reflectances, the Macbeth ColorChecker Chart (Gretag-Macbeth, X-Rite Inc., Grand Rapids, MI), widely used in camera characterization 10,30 and spectral recovery. 35 Five different sets of 240 reflectances from test set 1 were randomly selected, and the performance of the recovery matrix for this set was compared with that for the set of ColorChecker reflectances.…”

Section: Performance With the Macbeth Colorcheckermentioning

confidence: 99%

“…Many multispectral-imaging methods exploit the underlying smoothness of signal spectra, 10 with illuminants [11][12][13] and spectral reflectances 14,15 represented by low-dimensional models based on principal component analysis (PCA) or independent component analysis (ICA). [16][17][18][19][20] Thus, given a linear model, 10,21 if the number of PCA or ICA coefficients of a particular set of spectra is the same as the number of camera responses (three in the simplest trichromatic case), then the spectra can be derived by an inverse transformation of the set of camera responses, with the forward transformation being estimated from a representative (''training'') data set.…”

Section: Introductionmentioning

confidence: 99%

“…[16][17][18][19][20] Thus, given a linear model, 10,21 if the number of PCA or ICA coefficients of a particular set of spectra is the same as the number of camera responses (three in the simplest trichromatic case), then the spectra can be derived by an inverse transformation of the set of camera responses, with the forward transformation being estimated from a representative (''training'') data set. If the number of coefficients is more than the number of response values, then the latter may need to be increased by imaging the scene under different illuminants or by introducing colored fil- ters one at a time in front of the camera to modify the sensor spectra.…”

Section: Introductionmentioning

confidence: 99%

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Recovering spectral data from natural scenes with an RGB digital camera and colored filters

Valero

Nieves

Nascimento

et al. 2007

Color Research & Application

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Many spectral-recovery methods using RGB digital cameras assume the underlying smoothness of illuminant and reflectance spectra, and apply low-dimensional linear models. The aim of the present work was to test whether a direct-mapping method could be used instead of a linear-models approach to recover spectral radiances and reflectances from natural scenes with an RGB digital camera and colored filters. In computer simulations, a conventional RGB digital camera with up to three colored filters was used to image scenes drawn from a hyperspectral image database. Three measures were used to evaluate recovery with the direct-mapping method: goodness-of-fit, root-mean-square error, and a color-difference metric. It was found that with two and three filters both spectral radiances and reflectances could be recovered sufficiently accurately for many practical applications. With little increase in computational complexity, an RGB camera and a few colored filters can provide significantly better recovery of natural scenes than an RGB camera alone.

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Section: Performance With the Macbeth Colorcheckermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recovering spectral data from natural scenes with an RGB digital camera and colored filters

Valero

Nieves

Nascimento

et al. 2007

Color Research & Application

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“…Commercially available digital cameras usually produce devicedependent color outputs (RGB, sRGB, AdobeRGB etc.). In the case of applications that require ultra high quality of the visual information, device-independent color data is needed [1]- [3]. The target of this research is the ability to develop a relatively inexpensive camera system with outstanding color acquisition performance.…”

Section: Introductionmentioning

confidence: 99%

Development of an XYZ Digital Camera with Embedded Color Calibration System for Accurate Color Acquisition

Kretkowski

Jabłoński

Shimodaira

2010

IEICE Trans. Inf. & Syst.

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SUMMARYAcquisition of accurate colors is important in the modern era of widespread exchange of electronic multimedia. The variety of device-dependent color spaces causes troubles with accurate color reproduction. In this paper we present the outlines of accomplished digital camera system with device-independent output formed from tristimulus XYZ values. The outstanding accuracy and fidelity of acquired color is achieved in our system by employing an embedded color calibration system based on emissive device generating reference calibration colors with user-defined spectral distribution and chromaticity coordinates. The system was tested by calibrating the camera using 24 reference colors spectrally reproduced from 24 color patches of the Macbeth Chart. The average color difference (CIEDE2000) has been found to be ΔE = 0.83, which is an outstanding result compared to commercially available digital cameras.

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“…This may be one of the reasons why the color accuracy of colorimetric characterization is sometimes better than that of spectral characterization. 6 In this Letter, we propose a method for the accurate estimation of spectral reflectance from the responses of a color scanner with the consideration of approximately perceptually uniform color space CIELAB. To reduce errors in reflectance estimation, two approaches, namely, colorimetric-based spectral calculation and weighted spectral calculation, are involved in this method.…”

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confidence: 99%

Estimation of spectral reflectance of object surfaces with the consideration of perceptual color space

Shen

Xin

2006

Opt. Lett.

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Due to the nonlinear transform from spectral reflectance to CIELAB values, the solution obtained by minimizing reflectance error in traditional spectral characterization methods will not be optimal when evaluated by colorimetric error. We propose a reflectance estimation method with consideration of perceptual color space. It combines two approaches, i.e., colorimetric-based spectral calculation and weighted spectral calculation. The experimental results show that the proposed method performs better than previous methods in terms of color difference with very slight degradation in spectral accuracy. © 2006 Optical Society of America OCIS codes: 330.1710, 330.1730 In many applications such as industrial color quality control and art painting archiving, there is an increasing need to estimate the spectral reflectance of object surfaces using a digital camera or a color scanner. Shi and Healey 1 presented a spectral characterization method to recover reflectance from scanner responses based on the linear reflectance model (LRM) and showed that it outperformed the traditional polynomial transform.2 Dicarlo and Wandell 3 found that the statistical distribution of spectral reflectance had an important influence on estimation accuracy. Inspired by that finding, Shen and Xin recently proposed two methods, namely, adaptive estimation 4 (AE) and optimized adaptive estimation (OAE), 5 for reflectance recovery by training sample selection and weighting. However, due to the color matching functions and the nonlinear transform between CIEXYZ values and CIELAB values, the minimization of reflectance error of previous methods does not guarantee an optimal solution in terms of color difference. This may be one of the reasons why the color accuracy of colorimetric characterization is sometimes better than that of spectral characterization. 6In this Letter, we propose a method for the accurate estimation of spectral reflectance from the responses of a color scanner with the consideration of approximately perceptually uniform color space CIELAB. To reduce errors in reflectance estimation, two approaches, namely, colorimetric-based spectral calculation and weighted spectral calculation, are involved in this method.We suppose that the visible spectrum, from 400 to 700 nm, is equally sampled in N wavelengths. For an ideal three-channel color scanner, its 3 ϫ 1 response vector u is simply the product of the 3 ϫ N matrix M s of spectral responsivity (including spectral radiance of the illuminant and spectral sensitivity of sensors) and the N ϫ 1 vector r of spectral reflectance, i.e., u = M s r. For a common scanner however, its 3 ϫ 1 actual response vector may carry some influence from a nonlinear optoelectronic conversion function (OECF) F OECF ͑·͒ and a 3 ϫ 1 response bias vector f caused by dark current. Hence the imaging process is formulated aswhere v = M s r + f. As F OECF ͑·͒ can be obtained using the actual responses and mean reflectance (or luminance) of gray-scale patches, 4 the crucial equation reads ͑2͒from which M s ...

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Characterization of trichromatic color cameras by using a new multispectral imaging technique

Cited by 96 publications

References 15 publications

Recovering spectral data from natural scenes with an RGB digital camera and colored filters

Recovering spectral data from natural scenes with an RGB digital camera and colored filters

Development of an XYZ Digital Camera with Embedded Color Calibration System for Accurate Color Acquisition

Estimation of spectral reflectance of object surfaces with the consideration of perceptual color space

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