Studies On Bionic Optimization Algorithms And Their Applications In Digital Image Processing

Digital image processing is one of the most active areas of computer applications.From the invention of CT,the popularity of digital cameras and the development of digital television services,to the applications of remote sensing image processing,biometric identification and intelligent transportation,digital image processing applications can be seen everywhere,which has greatly promoted the scientific researches,changed the way of social life and increased productivity.As a result,digital image processing,as a discipline of broad prospect of applications,still faces many problems have yet to be explored both in theoretical research and in applications.Bionic optimization algorithms,simulating the structural characteristics,law of evolution,behavior patterns,and way of thinking of biological or biological population,are computing methods with self-organization,adaptive and self-learning abilities,as well as a good global convergence,parallelism,and robustness.The commonly used bionic optimization algorithms include artificial neural network, genetic algorithm,ant colony algorithm,and so on.Digital image processing is a complex problem solving,and the bionic optimization is particularly well suited to deal with those complex and nonlinear problems that traditional search methods are difficult to solve,such as in the field of combinatorial optimization.As a result,there is a trend in recent years taking digital image processing as a combinatorial optimization problem to study,and adopting a series of optimization strategies to carry out image processing tasks.This thesis puts forward some new ideas and approaches on applying bionic optimization algorithms,such as article artificial neural networks,genetic algorithm and ant colony algorithm,to digital image processing.This work is summarized as follows:Systematically summed up the basic principles and the stat of the art of artificial neural networks,genetic algorithm,and ant colony algorithm,focusing on the ways to improve ant colony algorithm. Studied the image restoration method based on self-organizing neural network, proposed an image target recognition algorithm based on Hopfield neural network, and analyzed the algorithm and related experimental results.Investigated the image restoration method based on genetic algorithm,and the image segmentation processing method based on genetic algorithm;put forward a new image segmentation processing method using genetic algorithm based on fuzzy membership surface;by comparative analysis of the segmentation effects of different images,verified the feasibility of the algorithm.Proposed an ant-colony-algorithm-based image segmentation means clustering algorithm.By exploring the characteristics of combinational optimization,studied an ant-colony-algorithm-based modulus maximum reconstruction image compression algorithm,which is of simple structure and effective experimental results.