Genetic algorithms applied to bayesian image restoration
Kazunori Takatsu, Hidefumi Sawai, Sumio Watanabe, Masahide Yoneyama · Systems and Computers in Japan · 1995
Abstract This paper describes genetic algorithms (GAs) applied to Bayesian image restoration. Crossover operation is discussed as a method used to find new search points in GAs. A suitable genotype and an efficient crossover method for a Bayesian image restoration will be proposed. Due to the independency of GAs from objective function forms, it is easy to introduce ideas into the objective functions. The idea of line process proposed by S. Geman and D. Geman [12] is introduced into the objective function, which provides better restored images. As a comparison, a stochastic relaxation (SR) method is applied to the image restoration problem, and the convergence properties of SR and GAs are also discussed.