A fast parallel projection algorithm for set theoretic image recovery

A new projection algorithm for convex set theoretic image recovery [reconstruction and restoration] is presented. This algorithm comprises all serial and parallel projection methods as particular cases and is straightforwardly implementable on concurrent processors. It proceeds by taking convex combinations of selected projections at each iteration and allows extrapolated relaxations far beyond the range [0,2] used in conventional algorithms. These extrapolated, iteration-dependent relaxations result in very fast convergence. Numerical results are provided which show that the proposed algorithm outperforms existing ones, in particular the popular cyclic method of projections onto convex sets [POCS].<>