Regression methods are widely used to estimate the spectral reflectance of object surfaces from camera responses. These methods are under the same problem setting as that to build an estimation function for each sampled wavelength separately, which means that the accuracy of the spectral estimation will be reduced when the training set is small. To improve the spectral estimation accuracy, we propose a novel estimating approach based on the support vector regression method. The proposed approach utilizes a composite modeling scheme, which formulates the RGB values and the sampled wavelength together as the input term to make the most use of the information from the training samples. Experimental results show that the proposed method can improve the recovery accuracy when the training set is small.
© 2008 Optical Society of America
Original Manuscript: March 6, 2008
Revised Manuscript: June 1, 2008
Manuscript Accepted: June 30, 2008
Published: August 18, 2008
Vol. 3, Iss. 11 Virtual Journal for Biomedical Optics
Wei-Feng Zhang and Dao-Qing Dai, "Spectral reflectance estimation from camera responses by support vector regression and a composite model," J. Opt. Soc. Am. A 25, 2286-2296 (2008)