In order to solve the problem of low accuracy and big error in hill climb searching method caused by the unimodal focal value function, a new autofocus method based on blur difference qualitative analysis was presented. First, the spatial-domain convolution/deconvolution transform was used to compute the blur difference at every point of two probed images corresponding to two different focus positions. Second, blur difference qualitative measurement of two images was made by voting policy. Then, the searching direction was determined by blur difference qualitative measurement of two probed images. Finally, using the variable step scheme, the searching range was gradually narrowed down and the searching steps was reduced until the best focus position was found. Three image sequences of difference focus positions were collected by an 18X zoom surveillance camera. The experimental result indicates that, compared with two typical methods based on focal value function, the proposed method keeps the advantages of the hill climb searching method with increasing the accuracy and reducing the error, and resolves the influence of local minima.

Concerning the problems of low accuracy, limitations of stagnation and slow convergence speed in the later evolution process of Artificial Fish Swarm Algorithm (AFSA), a Parallel Dynamic weigh Niches Artificial Fish Swarm (PDN-AFS) algorithm based on multi-core cluster was proposed. Firstly, the advantages and disadvantages of AFSA were analyzed, and dynamic weighting factor strategy and niche mechanism were adopted, hence a new Dynamic weigh Niches Artificial Fish Swarm (DN-AFS) algorithm was put forward. Then parallel design and analysis of DN-AFS algorithm based on parallel programming model (MPI+OpenMP) were introduced. Finally, the simulation experiments on multi-core cluster environment were given. The experimental results show that PDN-AFS can effectively improve the convergence speed and optimization performance of the complex multimodal function optimization problem, and achieve high speed ratio.