Spectral images are the most valuable data than can be achieved using 2D sensors. Spectral estimation using data with a few channel cameras has been the subject of many studies. It is common to use color filters in front of the lens for increasing dimensionality of data. However, spectral estimations are prone to suffer from colorimetric errors. To address this problem it was shown that this problem is a special case of error-free spectral estimation problem. Considering the fact that most of RGB cameras tend to be colorimetric, using geometrical modeling of the problem, it was shown that adding a shoot with bare lens to the sensor’s data can solve the problem. The notion has been tested in different scenarios and the efficiency of the proposed method has been proved in the scenarios. Results showed that if the camera is acceptably colorimetric, the proposed method can even leads to error-free colorimetric performance.
F. Martínez-Verdú, J. Pujol, P. Capilla, Characterization of a Digital Camera as an Absolute Tristimulus Colorimeter, J. Imaging Sci. Technol., 47(2003), 279-295.
P. L. Vora, H. J. Trussell, Measure of goodness of a set of color-scanning filters, J. Opt. Soc. Am. A, 10(1993), 1499-1508.
A. Mahmoudi Nahavandi, M. Amani Tehran, Metric for evaluation of filter efficiency in spectral cameras, Appl. Opt., 55(2016), 9193-9204.
A. Mahmoudi Nahavandi, M. Amani Tehran, A new manufacturable filter design approach for spectral reflectance estimation, Color Res. Appl., 42(2016), 316-326.
A. Mahmoudi Nahavandi, M. Amani Tehran, Image-based spectral transmission estimation using sensitivity comparison, Appl. Opt., 56(2017), 417-423.
R. S. Berns, L. A. Taplin, M. Nezamabadi, Spectral imaging using a commercial color-filter array digital camera, in The Fourteenth Triennial ICOM-CC meeting, The Hague, Netherlands, (2005), 743-750.
S. Tsutsumi, M. R. Rosen, R. S. Berns, Spectral color management using interim connection spaces based on spectral decomposition, Color Res. Appl., 33(2008), 282-299.
W. K. Pratt, Digital Image Processing, 3rd Edition ed.,John Wiley & Sons, Los Altos, California, 2001, 356-365.
D. Connah, S. Westland, M. G. A. Thomson, Recovering spectral information using digital camera systems, Color. Technol., 117(2001), 309-312.
E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, D. H. Foster, Recovering spectral data from natural scenes with an RGB digital camera and colored filters, Color Res. Appl., 32(2007), 352-360.
S. G. Kandi, Spectral Estimation of Printed Colors Using a Scanner, Conventional Color Filters and Applying Backpropagation Neural Network, Prog. Color Colorants Coat., 4(2011), 39-50.
R. Jafari, S. H. Amirshahi, Spectral Reconstruction of Blacks and Whites by Using the Statistical Colorants Prog. Color Colorants Coat., 8(2015), 135-144.
P. C. Hansen, Regularization Tools, A Matlab Package for Analysis and Solution of Discrete Ill-Posed Problems, T. U. o. D. Informatics and Mathematical Modelling Building 321, DK-2800 Lyngby, ed.Denmark, (2008).
S. Han, Y. Matsushita, I. Sato, T. Okabe, Y. Sato, Camera spectral sensitivity estimation from a single image under unknown illumination by using fluorescence, in 2012 IEEE Conference on Computer Vision and Pattern Recognition, (2012), 805-812.
J. Jiang, D. Liu, J. Gu, S. Süsstrunk, What is the space of spectral sensitivity functions for digital color cameras?, in 2013 IEEE Workshop on Applications of Computer Vision (WACV), FL, USA, (2013), 168-179.
A. Mahmoudi Nahavandi, Noise segmentation for improving performance of Wiener filter method in spectral reflectance estimation, Color Res. Appl., 43(2018), 341-348.
H. Kuniba, R. S. Berns, Spectral sensitivity optimization of color image sensors considering photon shot noise, J. Electronic Imag., 18(2009), 1-14.
J. B. Cohen, W. E. Kappauf, Metameric Color Stimuli, Fundamental Metamers, and Wyszecki's Metameric Blacks, American J. Psychol., 95(1982), 537-564.
J. B. Cohen, W. E. Kappauf, Color Mixture and Fundamental Metamers: Theory, Algebra, Geometry, Application, American J. Psychol., 98(1985), 171-259.
G. Sharma, H. J. Trussell, Figures of merit for color scanners, IEEE Transactions Image Proc., 6(1997), 990-1001.
D. Coffin, Decoding raw digital photos in Linux (2015), retrieved 28 November, 2015, http://www. centrostudiprogressofotografico.it/en/dcraw/.
E. Kodak, Kodak Filters for Scientific and Technical Uses,Eastman Kodak Co, USA, 1982, 1-94.
F. H. Imai, R. S. Berns, D.-Y. Tzeng, A Comparative Analysis of Spectral Reflectance Estimated in Various Spaces Using a Trichromatic Camera System, J. Imaging Sci. Technol., 44(2000), 280-287.
D. C. Day, Filter Selection for Spectral Estimation Using a Trichromatic Camera, M.Sc., Rochester Institute of Technology Rochester , USA, 2003.
Y. Zhao, R. S. Berns, Image-based spectral reflectance reconstruction using the matrix R method, Color Research & Application, 32(2007), 343-351.
N. Du-Yong, J. P. Allebach, A subspace matching color filter design methodology for a multispectral imaging system, IEEE Transactions on Image Processing, 15(2006), 2631-2643.
S. Quan, N. Ohta, R. S. Berns, X. Jiang, Unified Measure of Goodness and Optimal Design of SpectralSensitivity Functions, Journal of Imaging Science and Technology, 46(2002), 14.
K. Fukunaga, Introduction to Statistical Pattern Recognition, Second Edition ed.,Morgan Kaufmann, San Francisco, CA, 1990, 30.
H. J. Trussell, A. Ö. Ercan, N. G. Kingsbury, Color filters: When “optimal” is not optimal, in 2016 IEEE International Conference on Image Processing (ICIP), Arizona, USA, (2016), 3987-3991.
Mahmoudi Nahavandi, A. (2020). Taking Full Advantage of RGB Sensor’s Colorimetric Characteristics in Multi-Spectral Imaging. Progress in Color, Colorants and Coatings, 13(2), 121-130. doi: 10.30509/pccc.2020.81616
MLA
A. Mahmoudi Nahavandi. "Taking Full Advantage of RGB Sensor’s Colorimetric Characteristics in Multi-Spectral Imaging", Progress in Color, Colorants and Coatings, 13, 2, 2020, 121-130. doi: 10.30509/pccc.2020.81616
HARVARD
Mahmoudi Nahavandi, A. (2020). 'Taking Full Advantage of RGB Sensor’s Colorimetric Characteristics in Multi-Spectral Imaging', Progress in Color, Colorants and Coatings, 13(2), pp. 121-130. doi: 10.30509/pccc.2020.81616
VANCOUVER
Mahmoudi Nahavandi, A. Taking Full Advantage of RGB Sensor’s Colorimetric Characteristics in Multi-Spectral Imaging. Progress in Color, Colorants and Coatings, 2020; 13(2): 121-130. doi: 10.30509/pccc.2020.81616