TY  - JOUR
AU  - A. Gijsenij
AU  - Theo Gevers
AU  - Joost Van de Weijer
PY  - 2010//
TI  - Generalized Gamut Mapping using Image Derivative Structures for Color Constancy
T2  - IJCV
JO  - International Journal of Computer Vision
SP  - 127
EP  - 139
VL  - 86
IS  - 2-3
PB  - Kluwer Academic Publishers Hingham, MA, USA
N2  - The gamut mapping algorithm is one of the most promising methods to achieve computational color constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n-jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated colors and diffuse light. Further, as the n-jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and realworld scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n-jet based gamut mapping, (3) the combination of the different n-jet based gamut
SN  - 0920-5691
UR  - http://dx.doi.org/10.1007/s11263-008-0171-3
N1  - ISE
ID  - A. Gijsenij2010
ER  -