基于GA-MCMC的粒子滤波图像恢复算法

Image Restoration Based on GA-MCMC Particle Filters

针对粒子滤波的退化和贫化问题,提出一种GA-MCMC粒子滤波图像恢复算法.该算法引入遗传算法(GA)全局寻优和粒子总数多样性的特性,结合马尔可夫链蒙特卡罗方法(MCMC)的收敛性,将交叉、变异和选择操作融入到粒子滤波图像恢复中,提高了粒子滤波的鲁棒性、精确性和灵活性.实验结果表明,该算法能减少贫化和退化问题,且在对具有混合噪声的真实图像恢复效果方面显示了其优越性.

Particle filter is applied in image restoration, in order to remove degeneracy phenomenon and alleviate the sample impoverishment problem. The global optimization and particle diversity of generic algorithm（GA） are introduced, and the convergence of Markov chain Monte Carlo （MCMC） method was combined, the crossover, mutation and selection operation were used in image restoration by particle filter, to enhance the robustness, accuracy and flexibility of the particle filter. Furthermore, a new image restoration algorithm by GA-MCMC particle filter is proposed. Simulation results showed that this method can reduce the impoverishment and degeneracy problems, and from the restoration results to mixed noisy image, we can see the effectiveness and superiority of the proposed algorithm.