Quantum Genetic Algorithm And Its Application On Image Auto-adaptive Optimization

Image Enhancement is done to make obscure image clear or draw out what we are interested in, so as to improve the vision sense of image or make the following image processing easier. The nonlinear transform of gray level is an efficient method in the field of image enhancement. This method uses four kinds of transform functions according to different states for the gray level images. To simulate the four kinds of transform functions mentioned above, Tubbs proposed a normalized incomplete Beta function. With the different values of arguments of normalized incomplete Beta function, different gray-transform functions can be simulated. But the previous method to decide arguments is based on endless enumeration and manual acting, which is very complex and unintelligent.Quantum Genetic Algorithm, which is based on the state vector denotation of quanta, is the combination of Quantum computation and Genetic Algorithm. Quantum Genetic Algorithm uses qubits to denote chromosome instead of binary or decimal coding in Genetic Algorithm, and quantum update gate as the genetic operation. Thus, Quantum Genetic Algorithm is more efficient than Genetic Algorithm.In this paper, QGA is used to optimize arguments adaptively according to the quality of image. Result of the experiment shows that Quantum Genetic algorithm has powerful searching ability, which can obtain good nonlinear transform curve according to the state of the gray level image. So this method is practical and efficient in the field of gray level image adaptive enhancement.