Prior Model for the MRF Modeling of Multi-Channel Images

In multi-channel images (e.g. color images with R, G, B channel, and multi-spectral images), there exist higher-order correlations among the channels. We develop a new MRP-MAP (Markov random field - maximum a posteriori) framework that can be used for various multi-channel image processing. Main features of the proposed framework is that the higher-order correlation between the channels is considered, whereas it is not well addressed in the conventional works. Given a channel image, the prior probability of another channel is computed based on the MRF modeling that the channel correlation is described as piecewise linear relationship. An optimization algorithm for the MAP estimation is also developed. The effectiveness of the proposed priors is demonstrated with a simple application, i.e., image denoising.