Optimal Learning High-Order MRF Priors of Color Image

In this paper, we present an optimised learning algorithm for high-order Markov random fields (MRF) color image priors that capture the statistics of natural scenes and can be used for a variety of computer vision tasks. The proposed optimal learning algorithm is achieved by simplifying the estimation of partition function in the learning model. The parameters in MRF color image priors are learned alteratively and iteratively by maximising their likelihood. We demonstrate the capability of the proposed learning algorithm of high-order MRF color image priors with the application of color image denoising. Experimental results show the superior performance of our algorithm compared to the state–of–the– art of color image priors in [1], although we use a much smaller training image set.