On the estimation of spectral data: a genetic algorithm approach

Spectral data estimation from image data is an ill-posed problem since (i) due to the integral nature of solid-state light sensors, the same output can be obtained from an infinity of input signals and (ii) color signals are spectrally smooth in nature and therefore limit the number of linear independent equations that can be formulated for the identification problem. To enable the solution of these problems most methods rely on exact a priori knowledge, such as smoothness and modality, to formulate hard constraints. A new method based on an extended generalized cross-validation measure is introduced for this type of problems. The solution is obtained with a genetic algorithm that maximizes its prediction ability. The method does not require exact a priori knowledge on the solution, since it is able to extract this information from the input data