A method using genetic algorithms for path generation have been proposed; however, this method is limited to particular applications, and there are limitations on the types of paths that can be represented. This paper therefore proposes a path generation method that is applicable to more general-purpose applications compared to previous methods based on a new design of the genotype used in the genetic algorithm.

In the field of the restoration of motion blurred images, several deterministic techniques are proposed. Motion blurred images, however, include an ambiguity intrinsically. Therefore, the statistical restoration techniques are suited for the aim. In this paper, we describe the statistical properties of images blurred by various motions in mean square error sense since the discussions on the subject have been not presented in the past. The mean square errors of images blurred by motions and of images restored by Wiener filtering are formulated. The relations between the mean square errors and the extent of the motion blur or the correlation coefficient of the object are presented. The differences of the mean square errors among the various motions are discussed. Furthermore, these formulations are expanded to the motion blurred images imbedded by additive noise.