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

JOSA A, Vol. 24, Issue 9, pp. 2673-2683 (2007)

http://dx.doi.org/10.1364/JOSAA.24.002673

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### 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.

© 2007 Optical Society of America

**OCIS Codes**

(100.3010) Image processing : Image reconstruction techniques

(100.3190) Image processing : Inverse problems

(330.1710) Vision, color, and visual optics : Color, measurement

**ToC Category:**

Image Processing

**History**

Original Manuscript: September 20, 2006

Revised Manuscript: March 23, 2007

Manuscript Accepted: April 13, 2007

Published: July 30, 2007

**Virtual Issues**

Vol. 2, Iss. 10 *Virtual Journal for Biomedical Optics*

**Citation**

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)

http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=josaa-24-9-2673

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