Quick Markov random field image fusion

The use of a Markov random field for both the restoration and segmentation of images is well know. It has also been shown that this framework can be extended and allow the fusion of data extracted from several images all registered with each other but from different sensors. The main limitation of these fusion methods is that they rely on the use of stochastic sampling methods and consequently are prohibitively slow, even with the use of dedicated processors. This has prevented the easy use of these methods in real time systems. Here an new approach to the fusion problem is taken. An alternative construction for the Markov random field is used. This concentrates only on the construction of the image boundary map, leaving the pixel values fixed. This coupled with the use of an appropriately designed Iterative Conditional Modes (ICM) algorithm, produces an algorithm which is significantly less expensive and, with the correct processor, it is hoped may be operated in real time.