Research Of Digital Image Processing Methods Based On Particle Swarm Optimization

Digital image processing technology has been widely used in the fields of military, medicine, remote sensing and industry and so on. Because of the complexity and diversity of image information, there are problems of imperfection, uncertainty and modeling difficulties in image processing field. To solve problems above, intelligent optimization algorithms have been widely adopted, which can achieve a better performance compared with other traditional methods in several respects. In recent years, the research of particle swarm optimization (PSO) has made some progress in the image processing field, but there are still many issues about image segmentation, image enhancement and image restoration worth further studying.On the basis of the research on fundamental theory of the PSO algorithms, an improved particle swarm optimization algorithm is presented in this dissertation. The theory of image fuzzy threshold segmentation, the clustering image segmentation, image enhancement and image restoration are researched based on the PSO algorithms combined with the fuzzy theory and simulated annealing algorithm. Main contributions of the dissertation are shown as following:1. A modified niching PSO based on the hill valley function is proposed. A new niching is formed by utilizing the algorithm which uses the hill valley function to judge whether the niching subswarms are produced and merged . The algorithm has solved the problems of choosing the initial parameters depending on prior knowledge and slow convergence speed . which improves searching ability of multiple solutions, avoids wasting computing resource, and makes significant improvement in searching efficiency and convergence speed.2. An image segmentation algorithm based on particle swarm optimization algorithm and maximum fuzzy entropy is put forward. The algorithm utilizes the global searching ability of the PSO and maximum fuzzy entropy theory, and searches the optimal combinations of the fuzzy parameters. It can adaptively obtain the segmentation thresholds, and can be used to the image segmentation for single, multiple objective and low signal-to-noise image. With the strong adaptability and good result, the algorithm can greatly reduce complexity of the calculations.3. A fuzzy clustering algorithm in image segmentation is proposed based on the PSO algorithm. The algorithm is applied to normal image,noise pollution image and color image segmentation by modifying the objective function of traditional FCM algorithm, establishing the fitness function to different application object and utilizing the predator-prey PSO algorithm to search the optimum clustering center . The algorithm has overcome the defects that the fuzzy C-means clustering algorithm is sensitive to the initial clustering center and involuntary to get into local optimum, so that the calculation speed of FCM algorithm is improved. Especially for noise image segmentation, this algorithm not only takes the fuzziness into consideration but also utilizes the spatial information and accordingly gets the characteristics of insensitive to noise, high anti-noise property and strong robustness.4. Image enhancing algorithm based on the PSO algorithm is proposed. The algorithm can automatically search for the optimal parameter of Beta function to achieve gray image adaptive enhancement by fitting contrast transform curve using regularized Beta function proposed by Tubbs. For color image, the algorithm can get the best weight value of filtering window adaptively, and reflects that spatial distance between two pixels in a filtering window has some influence on the filtering effect. The algorithm can also realize the adaptive filter of the pulse noise and have better performance compared to existing methods.5. A method of image restoration based on PSO and simulated annealing algorithm is proposed. The algorithm has overcome some problems of traditional algorithm such as more constraint conditions, depending prior knowledge, calculation complexity and noise susceptibility, and can be applied into the different restored image, and effectively overcomes the difficulties to determine noise-signal power ratio of Wiener filtering.At last, the research results are summarized, and some issues are raised for the further research.