A linear pseudo-inverse method for unsupervised illuminant recovery from natural scenes is presented. The algorithm, which uses a digital RGB camera, selects the naturally occurring bright areas (not necessarily the white ones) in natural images and converts the RGB digital counts directly into the spectral power distribution of the illuminants using a learning-based spectral procedure. Computations show a good spectral and colorimetric performance when only three sensors (a three-band RGB camera) are used. These results go against previous findings concerning the recovery of spectral reflectances and radiances, which claimed that the greater the number of sensors, the better the spectral performance. Combining the device with the appropriate computations can yield spectral information about objects and illuminants simultaneously, avoiding the need for spectroradiometric measurements. The method works well and needs neither a white reference located in the natural scene nor direct measurements of the spectral power distribution of the light.
© 2008 Optical Society of America
Original Manuscript: February 4, 2008
Revised Manuscript: May 23, 2008
Manuscript Accepted: May 30, 2008
Published: July 3, 2008
Vol. 3, Iss. 8 Virtual Journal for Biomedical Optics
Juan L. Nieves, Clara Plata, Eva M. Valero, and Javier Romero, "Unsupervised illuminant estimation from natural scenes: an RGB digital camera suffices," Appl. Opt. 47, 3574-3584 (2008)