Regularized multichannel restoration of color images using cross-validation

Multichannel images are the multiple image planes (channels) obtained by imaging the same scene using multiple sensors. The validity of multichannel restoration where both the within and between channel relations are incorporated has already been established using both stochastic and deterministic restoration filters. However, it has been demonstrated that stochastic multichannel filters are extremely sensitive to the estimates of the between channel statistics. In this paper deterministic multichannel filters are proposed that do not utilize any prior knowledge about the multichannel image and the noise. Regularization based on the multichannel Cross-Validation function is used to obtain these filters. Their relation to stochastic multichannel restoration filters is examined and a technique to estimate the variance of the multichannel noise is proposed. Finally, experiments are shown where proposed filters and noise variance estimators are tested using color images.