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.
© 2007 Optical Society of America
Original Manuscript: September 20, 2006
Revised Manuscript: March 23, 2007
Manuscript Accepted: April 13, 2007
Published: July 30, 2007
Vol. 2, Iss. 10 Virtual Journal for Biomedical Optics
Ville Heikkinen, Tuija Jetsu, Jussi Parkkinen, Markku Hauta-Kasari, Timo Jaaskelainen, and Seong Deok Lee, "Regularized learning framework in the estimation of reflectance spectra from camera responses," J. Opt. Soc. Am. A 24, 2673-2683 (2007)