Generalized graduated nonconvexity algorithm for maximum a posteriori image estimation

An energy function for maximum a posteriori (MAP) image estimation is presented. The energy function is highly nonconvex, and finding the global minimum is a nontrival problem. When constraints on the interactions between line processes are removed, the deterministic, graduated nonconvexity (GNC) algorithm has been shown to find close to optimum solutions. The GNC model is generalized. Any number of constraints on the line processes can be added as a result of using the adiabatic approximation. The resulting algorithm is a combination of the conjugate gradient (CG) and the iterated conditional modes (ICM) algorithms and is completely deterministic. Since the GNC algorithm can be obtained as a special case of this approach, the algorithm is called the generalized GNC or G/sup 2/NC algorithm. It is executed on two aerial images. Results are presented along with comparisons to the GNC algorithm.<
>