A Class Of Parallel Iterative Median-Type Algorithms For Efficient Image Processing

This paper proposes a class of parallel iterative median-type filters to restore signals degraded by Gaussian noise. An efficient implementation of these filters on distributed memory MIMD machines is outlined. Iterative median-type algorithms have been implemented with high efficiency (? 90%) on the Fujitsu AP1000. We conclude that algorithms of this type are very suitable for parallel implementation on distributed memory machines. 1. Introduction Signal restoration, especially image restoration, requires large-volume and high-speed computations. Certain classes of image restoration operations such as spatial frequency filtering and convolution have relatively straightforward parallel implementations based on 2-D FFTs [1, 4, 5, 7, 11]. This paper discusses the more difficult case of non-linear filtering operations, with reference to application of a class of iterative median-type filters [2, 3, 4, 6, 8, 9, 10, 15] for enhancement of images degraded by additive Gaussian noise. The con..