Abstract
Genetic algorithms (GAs) represent a class of highly parallel adaptive search processes for solving a wide range of optimization and machine learning problems. The authors attempt to demonstrate the suitability of GAs in the automatic selection of the image enhancement operator for an unknown image. The problem is to select automatically an optimum set of 12 parameter values of a generalized enhancement function that maximizes some fitness function. The algorithm used both spatial and grayness ambiguity measures as the fitness value. A multiple point genetic cross-over operation has been used for better convergence. The algorithm does not need iterative visual interaction and prior knowledge of image statistics to select the appropriate enhancement function. Convergence of the algorithm was experimentally verified. >
Published Version
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