Multispectral synthesis of daylight using a commercial digital CCD camera

Nieves, Juan Luis; Valero, Eva M.; Nascimento, Sérgio M. C.; Hernández‐Andrés, Javier; Romero, Javier

doi:10.1364/ao.44.005696

Cited by 47 publications

(43 citation statements)

References 15 publications

(24 reference statements)

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“…The present work has shown, however, that a combination of the direct-mapping method and a conventional RGB digital camera with a limited number of colored filters can provide acceptably accurate estimates, complementing related work on recovering illuminant spectra in natural scenes. 3,34,37,38 Of the different combinations of colored filters, best performance was obtained with three filters. In terms of the three measures, the recovered signal had a goodnessof-fit coefficient better than 99.0%, a root-mean-square error less than 5.0%, and a CIELAB color difference less than 0.27.…”

Section: Discussionmentioning

confidence: 99%

“…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%

“…This selection was based on a previous exploratory computational optimization of different filters with Gaussian spectral transmittances and variable bandwidths. 3 …”

Section: Camera Spectral Sensitivities and Colored Filtersmentioning

confidence: 99%

“…[1][2][3][4] Typically, a multispectral system consists of a digital camera coupled to a range of spectrally broad-band or narrow-band filters. If the number of filters is sufficiently large and their bandwidths are sufficiently small, as with a hyperspectral imaging system, 2,5,6 spectral data can be recovered exactly.…”

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: Discussionmentioning

confidence: 99%

Section: Direct Mapping Of Camera Responses and Spectramentioning

confidence: 99%

“…This selection was based on a previous exploratory computational optimization of different filters with Gaussian spectral transmittances and variable bandwidths. 3 …”

Section: Camera Spectral Sensitivities and Colored Filtersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Valero

Nieves

Nascimento

et al. 2007

Color Research & Application

Self Cite

View full text Add to dashboard Cite

show abstract

“…As described elsewhere, calibrating a digital camera to accurately record skylight L or L v is now rather straightforward [27,29], as is determining how lens transmissivity and pixel position vary with zenith angle for a given fisheye lens. Note that because most overcasts have a limited range of colors, L and L v differ by a near-constant scaling factor L v ∶L (typically, the standard deviation of L v ∶L < 0:5% of its mean value).…”

Section: Brightness Calibration Of a Skylight Cameramentioning

confidence: 99%

Observed brightness distributions in overcast skies

Lee

Devan

2008

Appl. Opt.

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Beneath most overcasts, clouds' motions and rapidly changing optical depths complicate mapping their angular distributions of luminance L v and visible-wavelength radiance L. Fisheye images of overcast skies taken with a radiometer-calibrated digital camera provide a useful new approach to solving this problem. Maps calculated from time-averaged images of individual overcasts not only show their brightness distributions in unprecedented detail, but they also help solve a long-standing puzzle about where brightness maxima of overcasts are actually located. When combined with simulated radiance distributions from MODTRAN4, our measured radiances also let us estimate the gradients of cloud thickness observed in some overcasts.

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Using weighted pseudo‐inverse method for reconstruction of reflectance spectra and analyzing the dataset in terms of normality

Vahid

Amirshahi

Agahian

2011

Color Research & Application

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Most of spectral estimation methods are based on improving the learning-based procedures which mainly modify the training sets used by the basic methods. In this article, a new method is developed for analyzing of superiority of these modified processes to the basic methods in terms of normality of datasets. Hence, two qualitative terms, named generality and similarity are introduced to interpret the recovery achievements of different databases and their roles as training and testing sets. Also, a simple technique based on dataset modification of pseudo-inverse method is introduced for the recovery of reflectance spectra of samples from their corresponding colorimetric data. The method modifies the training dataset according to the color specifications of test sample. In fact, different weighting matrices are employed as dynamic modifiers to improve the pseudo-inverse estimation as a simple recovery method. The employed datasets are examined in the self as well as cross test conditions and the results are spectrally and colorimetrically evaluated. The root mean square errors between the reconstructed and actual spectra along with the corresponding color difference values under different illuminants decrease by employing the suggested modification method in comparison to classical pseudo-inverse technique as well as the recently improved version named optimized adaptive Wiener method.

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Multispectral synthesis of daylight using a commercial digital CCD camera

Cited by 47 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

Observed brightness distributions in overcast skies

Using weighted pseudo‐inverse method for reconstruction of reflectance spectra and analyzing the dataset in terms of normality

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