Spectral based Illumination Estimation and Color Correction
Reiner Lenz(1) Peter Meer(2) M. Hauta-Kasari(3)
(1)Image Processing Laboratory
Department of Electrical Engineering
Linköping University, S-58183 Linköping, Sweden
(2)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08855, USA
(3)Graduate School of Science and Engineering
Saitama University, 255 Shimo-okubo
Urawa, Saitama 388, Japan
We present a statistical technique to characterize the global color
distribution in an image. The result can be used for color
correction of a single image and for comparison of different images.
It is assumed that the object colors are similar to those in a set of
colors for which spectral reflectances are available (in our
experiments we use spectral measurements of the Munsell and NCS color
chips). The logarithm of the spectra can be approximated by finite
linear combinations of a small number of basis vectors.
We characterize the distributions of the expansion coefficients in an
image by their modes (the most probable values). This description does
not require the assumption of a special class of probability
distributions and it is insensitive to outliers and other
of the distributions. A change of illumination results in a global
shift of the expansion coefficients and thus also their modes.
The recovery of the illuminant is thus reduced to estimating
these shift parameters.
The calculated light distribution is only an estimate of the true
spectral distribution of the illuminant. Direct inverse filtering for
normalization may lead to undesirable results since these processes
are often ill-defined. Therefore we apply regularization techniques
in applications (such as automatic color correction) where visual
appearance is important. We also demonstrate how to use this
characterization of the global color distribution in an image as a
tool in color-based search in image databases.
Color Research and Application, 24, 98-111, 1999.
A shorter version appeared in
International Conference on Acoustics Speech and Signal
Processing, Munich, April 1997, 3141-3144.