Parallel optimization of stack filters

Abstract

An open problem associated with designing stack filters is finding the optimum configuration for a given noise type and the signal characteristics which need to be preserved. This problem is modeled here as a combinatorial search problem. Efficient search methods that can be easily implemented on any massively parallel computer were developed and tested in two parallel computing environments. The first is the Connection Machine, and the second is a hypercube-connected MIMD (multiple-instruction-stream, multiple-data-stream) machine simulated using Cosmic C. The performance of the filters found by the algorithms developed were excellent in comparison with the performance of the median filter. The efficiency of the algorithms clearly demonstrates the potential of using them for adaptive filtering. The algorithms can be implemented on any type of parallel computer.<>