Research On Particle Swarm Optimization And Its Application In Image Processing

As a member of swarm intelligence, Particle Swarm Optimization (PSO) provides new ideas to sovle those difficult problems for those traditional optimization methods. For its easy implementation, low requirement and cheap cost, PSO has used in wide engineering fields. Similar to other optimization algorithms, diversity loss in optimization procedure may cause local minima. Inspired by biological or physical models, multi-subswarms optimization could do well in avoiding local minima. But most multi-subswarms optimization algorithms are proposed for sovling special problem, general frame is useful to analyze or design multi-subswarms optimization.The main research work in the dissertation is as follows:(1) The r-selection and K-selection strategies in Ecology are introduced into particle swarm, and the r/KPSO is proposed (r-selection and K-selection based Particle Swarm Optimization, r/KPSO). The swarm is devided into two subswarms, r-subswarm favoring r-selection strategy and K-subswarm favoring K-selection strategy. The main task of r-subswarm is to explore the search space in quite high speed and r-paricles can breed many progencies. K-paritlces only breed few offsprings, and the offspring exploit the search space around their parent. The two subswarms compete and collaborate for the purpose of optimization. To evaluate the speed of convergence quantitatively, fisrt converging generation (FCG) is introduced to tell the first genertation where convergence begins. Some experiments on type benchmark functions showed that r/KPSO did well in most cases.(2) Based on r/KPSO, a PSO frame named Multi-Subswarms Multi-Strategies (MSMS) is provided for the analysis of multiswarm optimization. MSMS allows subswarms adopte various strategies, and the strategy favoring degree (SFD) can be used for evaluate the weight of certain strategy for the subswarm. All subswarms in MSMS can update synchronizingly or asynchronously. MSMS frame can be used for PSO structure analysis or design. As two examples, the OPSO and QSO are analyzed under MSMS frame. In the view of MSMS, r/KPSO is summarized and the ideas to improve it are also discussed.(3) Combining the PSO algorithm and wavelet neural network, the PSOWNN (Particle Swarm Optimized Wavelet Neural Network) is presented. PSOWNN can adjust its own structure by adding new neuron according to the network structure update principles. In the WNN training, PSO adoptes so-called"double cycles"structure, one for particle optimizing and another for structure adjusting. The test of pixles classifying proved the approximation performance of PSOWNN.(4) A noval denoising algorithm named MMFC (Modified Median Filtering and Classifying) is used to remove pulse noise. Two approaches (App1 and App2) of MMFC are provided and both of which adopte PSOWNN to classifying the pixels. If PSOWNN distinguishes the uncorrupted pixcels in the result image, App1 sets them as the values in noisy image. If PSOWNN distinguishes the corrupted pixels, App2 filters them and keeps others as before. For the classifying of PSOWNN, MMFC can process those pixels more properly than traditional median filtering.(5) Hierarchical Subbands Shrinking (HSS) is proposed for Gaussian noise removal. HSS adoptes special threshold determined by PSO for every subband. A noval thresholding function, smooth-thresholding (ST) is proposed and adopted by HSS. The ST function is suite for mathematic processing for it is continuous and derivatable for all real numbers.(6) Many variables need to be determined in image fusion based on wavelet region local statistics. A noval image fusion algorithm named PSOWR (Particle Swarm Optimized Wavelet Region) is proposed. The thresholds and other parameters used by image fusion are all determined by PSO and the fusion rules like local energy, contrast, weighted average and selection are combined with"region"idea for coefficient selection in the low- and high-pass subbands. The experiments on remote sense and medical images showed PSOWR can provide a more satisfactory fusion outcome.