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Heikkinen V, Jetsu T, Parkkinen J, et al. Regularized learning framework in the estimation of reflectance spectra from camera responses. J Opt Soc Am A Opt Image Sci Vis. 2007;24(9):2673-83doi: 10.1364/josaa.24.002673.
Heikkinen, V., Jetsu, T., Parkkinen, J., Hauta-Kasari, M., Jaaskelainen, T., & Lee, S. D. (2007). Regularized learning framework in the estimation of reflectance spectra from camera responses. Journal of the Optical Society of America. A, Optics, image science, and vision, 24(9), 2673-83. https://doi.org/10.1364/josaa.24.002673
Heikkinen, Ville, et al. "Regularized learning framework in the estimation of reflectance spectra from camera responses." Journal of the Optical Society of America. A, Optics, image science, and vision vol. 24,9 (2007): 2673-83. doi: https://doi.org/10.1364/josaa.24.002673
Heikkinen V, Jetsu T, Parkkinen J, Hauta-Kasari M, Jaaskelainen T, Lee SD. Regularized learning framework in the estimation of reflectance spectra from camera responses. J Opt Soc Am A Opt Image Sci Vis. 2007 Sep;24(9):2673-83. doi: 10.1364/josaa.24.002673. PMID: 17767236.
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J Opt Soc Am A Opt Image Sci Vis. 2007 Sep;24(9):2673-83. doi: 10.1364/josaa.24.002673.

Regularized learning framework in the estimation of reflectance spectra from camera responses.

Journal of the Optical Society of America. A, Optics, image science, and vision

Ville Heikkinen, Tuija Jetsu, Jussi Parkkinen, Markku Hauta-Kasari, Timo Jaaskelainen, Seong Deok Lee

Affiliations

  1. InFotonics Center, University of Joensuu, Finland. [email protected]

PMID: 17767236 DOI: 10.1364/josaa.24.002673

Abstract

For digital cameras, device-dependent pixel values describe the camera's response to the incoming spectrum of light. We convert device-dependent RGB values to device- and illuminant-independent reflectance spectra. Simple regularization methods with widely used polynomial modeling provide an efficient approach for this conversion. We also introduce a more general framework for spectral estimation: regularized least-squares regression in reproducing kernel Hilbert spaces (RKHS). Obtained results show that the regularization framework provides an efficient approach for enhancing the generalization properties of the models.

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