Dimensionality Reduction of Multi-spectral Images for Color Reproduction

A new nonlinear dimensionality reduction method for multi-spectral images was presented to solve the problem brought by high dimensionality of multi-spectral images during color reproduction. Firstly, according to the characteristics of human visual system, the CIE standard observer color matching functions were weighted to the source spectral reflectance and then a principal component analysis (PCA) method was used to the weighted spectrum. This effectively improved the colorimetric precision and color difference stability of dimensionality reduction. Then for the spectral reflectance loss caused by weighting color matching functions, a PCA method was imposed on the lost spectrum to compensate the lost spectral accuracy caused by the improvement of colorimetric precision. This effectively improved the spectral precision of dimensionality reduction. Finally the principal components obtained from the first two steps were combined to form the low-dimensional spectral data. Experiments show that the new method outperforms the existing methods in the colorimetric accuracy, spectral accuracy and color difference stability under different illuminant.