System design for accurately estimating the spectral reflectance of art paintings

Haneishi, Hideaki; Hasegawa, Takayuki; Hosoi, Asako; Yokoyama, Yayoi; Tsumura, Norimichi; Miyake, Yoichi

doi:10.1364/ao.39.006621

Cited by 191 publications

(150 citation statements)

References 15 publications

(18 reference statements)

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“…Recently, multispectral imaging has been widely studied in the reconstruction of the spectral information of the samples being imaged and the recovery of the spectrums of illuminants [1][2][3][4][5][6][7][8][9][10]. In a multispectral imaging system, usually more than three imaging channels are used.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature of color imaging, several reflectance reconstruction techniques, such as Wiener estimation [2][3][4][8][9][10], pseudoinverse method [1, 7,11,12], and finite-dimensional modeling [4,11], have been proposed. Among these techniques, Wiener estimation is among the most widely adopted ones, which involves three matrixes, i.e., spectral responsivity, autocorrelation matrix of reflectance, and imaging noise.…”

Section: Introductionmentioning

confidence: 99%

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Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation

et al. 2007

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Abstract:In multispectral imaging, Wiener estimation is widely adopted for the reconstruction of spectral reflectance. We propose an improved reflectance reconstruction method by adaptively selecting training samples for the autocorrelation matrix calculation in Wiener estimation, without a prior knowledge of the spectral information of the samples being imaged. The performance of the proposed adaptive Wiener estimation and the traditional method are compared in the cases of different channel numbers and noise levels. Experimental results show that the proposed method outperforms the traditional method in terms of both spectral and colorimetric prediction errors when the imaging channel number is 7 or less. When the imaging system consists of 11 or more channels, the color accuracy of the proposed method is slightly better than or becomes close to that of the traditional method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation

et al. 2007

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“…2(a) show higher variance in fluorescence background, which will affect the accuracy of reconstructed spectra as shown next. Table 2 shows the cumulative contribution ratio [25] of different PC numbers for spontaneous Raman spectra and SERS spectra. The cumulative contribution ratio refers to the ratio of the sum of eigenvalues corresponding to PCs of interest to the sum of all eigenvalues.…”

Section: Resultsmentioning

confidence: 99%

Optimization of advanced Wiener estimation methods for Raman reconstruction from narrow-band measurements in the presence of fluorescence background

Chen

Ong

Lin

et al. 2015

Biomed. Opt. Express

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Abstract:Raman spectroscopy has shown great potential in biomedical applications. However, intrinsically weak Raman signals cause slow data acquisition especially in Raman imaging. This problem can be overcome by narrow-band Raman imaging followed by spectral reconstruction. Our previous study has shown that Raman spectra free of fluorescence background can be reconstructed from narrow-band Raman measurements using traditional Wiener estimation. However, fluorescence-free Raman spectra are only available from those sophisticated Raman setups capable of fluorescence suppression. The reconstruction of Raman spectra with fluorescence background from narrow-band measurements is much more challenging due to the significant variation in fluorescence background. In this study, two advanced Wiener estimation methods, i.e. modified Wiener estimation and sequential weighted Wiener estimation, were optimized to achieve this goal. Both spontaneous Raman spectra and surface enhanced Raman spectra were evaluated. Compared with traditional Wiener estimation, two advanced methods showed significant improvement in the reconstruction of spontaneous Raman spectra. However, traditional Wiener estimation can work as effectively as the advanced methods for SERS spectra but much faster. The wise selection of these methods would enable accurate Raman reconstruction in a simple Raman setup without the function of fluorescence suppression for fast Raman imaging. Appl. Spectrosc. 50(6), 805-811 (1996). Spectrosc. 51(12), 1845-1848 (1997). 11. S. Chen, Y. H. Ong, and Q. Liu, "Fast reconstruction of Raman spectra from narrow-band measurements based on Wiener estimation," J. Raman Spectrosc. 44(6), 875-881 (2013). 12. J. Zhao, M. M. Carrabba, and F. S. Allen, "Automated fluorescence rejection using shifted excitation Raman difference spectroscopy," Appl. Spectrosc. 56(7), 834-845 (2002). 13. P. A. Mosier-Boss, S. Lieberman, and R. Newbery, "Fluorescence rejection in Raman spectroscopy by shiftedspectra, edge detection, and FFT filtering techniques," Appl. Spectrosc. 49(5), 630-638 (1995

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“…25 and 26). The direct-mapping method has been applied successfully to art imaging 22,27 and to illuminant estimation, 3 but it is unclear whether it remains efficient for radiance or reflectance estimation in conditions where the illumination is uncontrolled. Natural scenes, the subject of the present study, represent a special challenge for spectral-recovery methods.…”

Section: Direct Mapping Of Camera Responses and Spectramentioning

confidence: 99%

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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System design for accurately estimating the spectral reflectance of art paintings

Cited by 191 publications

References 15 publications

Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation

Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation

Optimization of advanced Wiener estimation methods for Raman reconstruction from narrow-band measurements in the presence of fluorescence background

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

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