Semi-iterative inference with hierarchical models
This paper deals with hierarchical Markov random field models. We propose to introduce new hierarchical models based on a hybrid structure which combines a spatial grid of a reduced size at the coarsest level with sub-trees appended below it, down to the finest level. These models circumvent the algorithmic drawbacks of grid-based models (computational load and/or great dependance on the initialization) and the modeling drawbacks of tree-based approaches (cumbersome and somehow artificial structure). The hybrid structure leads to algorithms that mix a non-iterative inference on sub-trees with an iterative deterministic inference at the top of the structure. Experiments on a synthetic image demonstrate the gains provided in terms of both computational efficiency and the quality of results. Then experiments on real aerial images illustrate the ability of hybrid models to perform the multiresolution and multispectral image fusion.