From Color Sensor Space to Feasible Reflectance Spectra

Santini, Simone; Schettini, Raimondo

doi:10.1109/tsp.2007.907838

Cited by 18 publications

(23 citation statements)

References 32 publications

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“…Various techniques can be applied in such cases: different versions of principal-component analysis (PCA) and nonnegative matrix transformation (NMT) have been applied to the spectral recovery of "outside color gamut data" [2][3][4][5][6][7][14][15][16][17][18][19][20]. In Table 1 we report the statistical values for various PCA and NMT techniques, as well as for our interpolation technique.…”

Section: Resultsmentioning

confidence: 99%

“…Recently there has been considerable interest in spectral reflectivity recovery methods in the field of color science [1][2][3][4][5][6][7] because of their fundamental importance, as well as their technological significance. In general, the color of an object is determined by the illumination conditions and the reflectivity of the surface material [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The spectral behavior of a colored object is known to be the fingerprint of the color, which is independent of the applied light source and the observer (for nonfluorescent materials). Spectral reflectivity recovery is also important for multispectral image processing, color consistency, color reconstruction, and color image processing [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…The solution also depends on the noise and initial conditions [11][12][13]. Two major methods for spectral reflectivity recovery are the pseudo-inverse technique [2][3][4][5][6][14][15][16][17] and the interpolation technique [7,[18][19][20]. In the pseudo-inverse technique, principal-component analysis [2][3][4][5][6][14][15][16] and nonnegative matrix transformation [7,17,[21][22][23] are developed with various adaptation techniques.…”

Section: Introductionmentioning

confidence: 99%

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Spectral Reflectivity Recovery from Tristimulus Values Using 3D Extrapolation with 3D Interpolation

Kim

Werner²,

Siminovitch

et al. 2014

Journal of the Optical Society of Korea

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We present a hybrid method for spectral reflectivity recovery, using 3D extrapolation as a supplemental method for 3D interpolation. The proposed 3D extrapolation is an extended version of 3D interpolation based on the barycentric algorithm. It is faster and more accurate than the conventional spectral-recovery techniques of principal-component analysis and nonnegative matrix transformation. Four different extrapolation techniques (based on nearest neighbors, circumcenters, in-centers, and centroids) are formulated and applied to recover spectral reflectivity. Under the standard conditions of a D65 illuminant and 1964 10° observer, all reflectivity data from 1269 Munsell color chips are successfully reconstructed. The superiority of the proposed method is demonstrated using statistical data to compare coefficients of correlation and determination. The proposed hybrid method can be applied for fast and accurate spectral reflectivity recovery in image processing.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spectral Reflectivity Recovery from Tristimulus Values Using 3D Extrapolation with 3D Interpolation

Kim

Werner²,

Siminovitch

et al. 2014

Journal of the Optical Society of Korea

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“…The RMSE and SCI values can clearly present the pros and cons of various reconstruction results . In addition, three additional terms, known as feasibility, smoothness, and naturalness, were also introduced to explore the usefulness of recovery methods in physical and geometrical views . The PI methods had relatively larger reconstruction errors unless a weighting process was considered .…”

Section: Introductionmentioning

confidence: 99%

Recovering spectral reflectance based on natural neighbor interpolation with model‐based metameric spectra of extreme points

Chou

Hsieh

Chen

2019

Color Research & Application

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In this article, we proposed a new method based on natural neighbor interpolation to recover the spectral reflectance of objects from an image captured by a traditional Red‐Green‐Blue (RGB) digital camera. The concept of model‐based metameric spectra of eight extreme points in the standard RGB (sRGB) color gamut was further introduced to ensure that almost all test samples in the entire gamut can be simply and properly recovered without needing the extrapolation or any other auxiliary techniques. The quasi‐Newton method was used to estimate iteratively the optimal parameters of these metameric spectra, satisfying the constraints of the gamut extreme points. Several experiments were performed. The effectiveness of the method with and without the metameric spectra was evaluated, including some performance comparisons with the principal component analysis (PCA) method of transformational type (classic PCA and weighted PCA) and that of interpolation type. The results showed that the proposed method greatly enlarged the accurately applicable domain of the interpolation strategy and offered spectra with feasibility and naturalness much superior to the PCA‐based methods. The proposed method was obviously better than the conventional interpolation ones, and had a similar performance with the weighted PCA method in terms of color difference.

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Numerical methods for smoothest reflectance reconstruction

Burns

2019

Color Research & Application

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Three numerical methods are presented for finding the smoothest reflectance curve associated with a given triplet of tristimulus values. The methods differ in how “smooth” is defined, and also differ in the domain of colors over which they are applicable. The first method is very quick and applies to any tristimulus values, but sometimes can yield reflectance curves with portions that fall outside the range 0 to 1. The second method applies to colors within the spectral locus (real colors) and guarantees that the reflectances produced are positive. The third method applies to colors within the object color solid (object colors) and guarantees that the reflectances fall within the range 0 to 1. The methods are shown to create reflectances that closely resemble those of real colors (natural and synthetic). Focus is given to implementing the numerical methods in very short MATLAB/Octave functions and to understanding the numerical behavior of the methods near the limits of their respective domains of applicability in terms of matrix conditioning and discretization artifacts.

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From Color Sensor Space to Feasible Reflectance Spectra

Cited by 18 publications

References 32 publications

Spectral Reflectivity Recovery from Tristimulus Values Using 3D Extrapolation with 3D Interpolation

Spectral Reflectivity Recovery from Tristimulus Values Using 3D Extrapolation with 3D Interpolation

Recovering spectral reflectance based on natural neighbor interpolation with model‐based metameric spectra of extreme points

Numerical methods for smoothest reflectance reconstruction

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